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Epidemiological models to inform policy for control of emerging plant disease

Summary of the impact

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.

Submitting Institution

University of Cambridge

Unit of Assessment

Biological Sciences

Summary Impact Type

Environmental

Research Subject Area(s)

Mathematical Sciences: Statistics
Economics: Applied Economics

Improved animal health and welfare and economic benefits for farmers from better management of parasites in livestock

Summary of the impact

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:

  • In 2011, industry benefited from research on blowfly strike which has provided scientific evidence that strategic early treatment of sheep reduces season-long disease risk and results in financial savings for farmers, particularly where earlier emergence of flies occurs in response to warming temperatures.
  • Between 2008 and 2012, farmers realised a 73% direct saving in the monitoring of gastrointestinal nematodes due to the development of a composite faecal worm egg count (FEC) test and a decrease of up to 75% in the number of treatments given to lambs.
  • Farmers and livestock benefited from the slower development of anthelmintic-resistant parasites as a result of targeted treatment using the composite FEC test developed.

Submitting Institution

University of Bristol

Unit of Assessment

Biological Sciences

Summary Impact Type

Environmental

Research Subject Area(s)

Agricultural and Veterinary Sciences: Animal Production, Veterinary Sciences
Medical and Health Sciences: Public Health and Health Services

Statistical methods are helping to control the spread of epidemics

Summary of the impact

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.

Submitting Institutions

University of Edinburgh,Heriot-Watt University

Unit of Assessment

Mathematical Sciences

Summary Impact Type

Health

Research Subject Area(s)

Mathematical Sciences: Statistics

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