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Since 2004, researchers in Cambridge have developed a series of generic and flexible models to predict the spread of plant diseases in agricultural, horticultural and natural environments. These now underpin policy decisions relating to the management and control of a number of such diseases, including sudden oak death and ash dieback in the UK (by Defra and the Forestry Commission), and sudden oak death in the US (by the United States Department of Agriculture). This has subsequently had an impact on how practitioners manage these diseases in the field, and on the environment through the implementation of disease mitigation strategies. In the case of ash dieback, the Cambridge work has also directly contributed to public involvement in mapping the spread of the disease.
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.
Researchers in the Epidemiology Group at the University of Warwick have an international reputation for high-quality mathematical modelling of human infectious diseases, with particular emphasis on population heterogeneity and variability. Such formulations and insights are an important component of predictive modelling performed by Public Health England (PHE), and are helping to shape national policy for a range of vaccine-preventable infections.
The Warwick group was instrumental in providing a range of real-time analyses and advice to UK authorities during the 2009 H1N1 (swine-flu) pandemic, acknowledged by the Department of Health (DoH) to be "fundamental to the construction of the UK's pandemic response" and making an important contribution to the overall programme which "led to the saving of many hundreds of millions of pounds of taxpayers money, while greatly increasing the health of the Nation". Modelling and analysis carried out at Warwick continue to provide insight into the control and containment of future pandemics and are considered "essential in determining UK pandemic policy".
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).
Collaborations funded through EPSRC Interact and RCUK UK-China Science Bridge resulted in QUB's advanced control research having important economic and environmental impact in China, Pakistan, Vietnam. This includes the creation of new core modules for the Shanghai Automation Instrumentation Co (SAIC) SUPMAX Distributed Control System series of products now in use for whole plant monitoring and control to maximise energy efficiency and reduce pollutant emissions. These products have since 2008 increased SAIC's revenue by over $50M p.a. Related networked monitoring technologies have been successfully deployed in Baosteel's hot-rolling production lines and in the Nantong Water Treatment Company that treats 20,000 tonnes of industrial waste water daily.
Despite increasing surveillance, outbreaks of bovine tuberculosis (bTB) in the UK have steadily increased over the past two decades, with the disease now costing an estimated £100 million per annum in test and slaughter costs, and compensation payments.
Research by Professor Wood and Drs McKinley and Conlan has determined that successful control efforts will depend upon within-herd surveillance and also on reducing reintroduction from external sources; these results have directly altered the Department of Environment, Food and Rural Affairs' (Defra) new (July 2013) bovine TB strategy for England, which directly cites Dr Conlan's research when justifying changes in proposed regulations. On publication this research prompted questions during bovine Tuberculosis debates in both Westminster and the Scottish Parliament by Andrew George (MP, St. Ives) and Helen Eadie (MSP, Cowdenbeath) respectively. The work has also received national and specialist media coverage raising public awareness and understanding of bTB control in cattle.
Interdisciplinary research at the Royal Veterinary College (RVC) has provided core evidence on which global efforts are based in order to eradicate one of the most economically damaging diseases of the cattle industry. The research findings have helped steer national programmes to eradicate Bovine Viral Diarrhoea (BVD) across Europe, South Asia and Australasia, reducing economic losses. Professor Joe Brownlie has additionally led pilot programmes in the UK, providing data for a national scheme, campaigned widely to highlight the issue and secured farming industry awareness and support through media exposure.
In July 2011, a fish disease simulator developed in the Department of Mathematical Sciences at the University of Liverpool was installed on computers at the Centre for Environment, Fisheries & Aquaculture Science (Cefas), an executive agency of the UK government Department for Environment, Food and Rural Affairs (Defra).
Since this date, the simulator has significantly improved the capability available to Cefas for understanding the likely spread of infectious diseases in the aquaculture industry of England and Wales, and enabled the optimisation of methods for the prevention and control of outbreaks. Specifically, a user-friendly interface enables Cefas to focus on particular diseases of concern, understand their specific pattern of spread and optimise methods for their control. The simulator is currently being used to develop contingency planning for outbreaks.
Koi Herpes Virus is a notifiable disease in the UK which can cause serious economic losses in coarse and ornamental carp. It is a viral disease which is highly contagious and can cause 100% mortality in infected fish. In 2010, the Department for the Environment Food and Rural Affairs (DEFRA) made a policy decision, based on our mathematical modelling and computational work, that they would not attempt to eradicate the disease because it would not be cost effective. They used the model predictions to carry out an economic analysis which took into account the cost of the predicted number of outbreaks and the cost of surveillance. They concluded that the benefit of an eradication programme, over a time period of 20 years, would range between a net cost of £213m and a net benefit of £8.36m with their best estimate being a net cost of £5.48m (Section 5, reference 1, paragraph 1.6).
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.