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Modelling the spread and control of foot-and-mouth disease in endemic regions

Principal Supervisor: Dr Michael Tildesley - School of Life Sciences

Co-supervisor: Professor Matt Keeling - Mathematics Institute

PhD project title: Modelling the spread and control of foot-and-mouth disease in endemic regions

University of Registration: University of Warwick

Project outline:

Foot-and-mouth disease (FMD) is a highly infectious, viral disease which primarily affects cloven-hoofed ruminants. Cattle, sheep, pigs, goats and deer are most severely affected, with the disease resulting in blisters on the mouth, feet, nose and udders of infected animals. Whilst many countries are free of disease, FMD circulates endemically in many regions of the world, having a significant impact upon farmers’ livelihoods, local economies and the export market. In addition, there remains a constant risk of introduction of FMD into disease free regions and with this in mind, it is crucial to understand the characteristics leading to persistence of the disease and to develop intervention strategies to reduce prevalence and minimize losses to stakeholders.

Mathematical models are extensively used in both public-health and veterinary-health policy planning. Modern predictive models are now at the heart of policy decisions, and such models are having an increasing role in supporting decisions associated with livestock infections. The modelling of FMD has a rich history, largely catalysed by the 2001 outbreak in the UK – which remains one of the best documented outbreaks. Work by Keeling & Tildesley has extended the model formulation developed in 2001, and applied the methodology to simulate future outbreaks and potential control in the UK and elsewhere. Previous work, in collaboration with EUFMD and local veterinary services, has begun to understand FMD transmission in Turkey where the disease is endemic. This preliminary model development will form a cornerstone of this project, allowing us to predict infection levels and persistence in endemic regions. In this project, we will therefore develop a hierarchy of models across a range of scales to maximize our use of available data. This cross-scales interaction is particularly important for the spread of FMD in endemic regions where within her dynamics and differing farming practices could profoundly impact the local or even national prevalence. In particular, we will develop herd level models that will be used to inform the models at larger spatial scale and will enable us to assess the model complexity required to make robust predictions at the population level scale. Finally, the models will be utilised to test scenarios and control options that may reduce virus transmission between different hosts, and reduce the overall prevalence of FMD in endemic regions. This will not only be beneficial in the endemic regions themselves but will also aid in contingency planning for potential future FMD outbreaks in the UK.

This project will improve our knowledge of the key factors that have led to the persistence and spread of FMD and strategies for control. The research project will require an interdisciplinary perspective, with a opportunity to develop expertise in a range of fields from landscape epidemiology, data analysis, statistical analysis and mathematical modelling. The resultant models that are developed will be used to inform intervention strategies in endemic countries and will therefore have an ongoing impact. The post holder will work closely with collaborators at EuFMD and the Food and Agriculture Organisation of the United Nations (FAO) to ensure that project results are communicated to regional policy makers and stakeholders throughout the project.

BBSRC Strategic Research Priority: Food security

Techniques that will be undertaken during the project:

The student will use a range of techniques for this project, including landscape epidemiology (to develop spatial maps of landscape features in “at risk” regions that will contribute to transmission risk), statistical methodologies such as Bayesian inference to parameterise models, to detailed spatial epidemiological modelling. These techniques will culminate in the development of models at a range of spatial scales that will inform the spread of disease and intervention strategies to minimize future disease risk.

Contact: Dr Mike Tildesley, University of Warwick