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 livestock zoonotic disease has a rich history, largely catalysed by the 2001 foot-and-mouth disease (FMD) outbreak in the UK – which remains one of the best documented outbreaks. Since 2001, the Warwick has applied the methodology to simulate future outbreaks and potential control in the UK and other countries around the world. Recent work has included the development of models to (i) understand FMD transmission in Turkey and Kenya where the disease is endemic, (ii) predict the spread of highly pathogenic avian influenza in South East Asia and (iii) investigate integrated surveillance strategies for canine rabies in the Philippines.
In this project, we will develop a hierarchy of models to simulate the spread of livestock and zoonotic disease across a range of scales to maximize our use of available data. This cross-scales interaction may be important for the spread of disease in endemic settings where within herd dynamics and differing farming practices could profoundly impact the local or even national prevalence. Research areas of interest will include (i) establishing the potential for disease emergence across different demographic settings, (ii) the ability of models to predict the spread of disease during emerging outbreaks, (iii) the role of surveillance and monitoring to reduce uncertainty in disease spread and (iv) the development of economically efficient intervention policies that can be reduce the spread of disease and (for zoonotic infections) the risk of transmission to humans.
This project will improve our knowledge of the key factors that may lead to outbreak emergence and strategies for control both in the early stages of new outbreaks and in endemic settings. A particular area of interest currently is in the use of surveillance to detect emerging outbreaks and to improve model predictions of epidemic spread and the impact of control. The research project will require an interdisciplinary perspective, with an 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 and surveillance strategies both in endemic settings and for contingency planning and will therefore have an ongoing impact. The post holder will work closely with collaborators such as the Food and Agriculture Organisation of the United Nations (FAO), the Department for the Environment, Food and Rural Affairs and government agencies around the world to ensure that project results are communicated to regional policy makers and stakeholders throughout the project.
References
- Tildesley, MJ, Brand, S, Brooks Pollock, E, Bradbury, NV, Werkman, M & Keeling, MJ (2019). The role of movement restrictions in limiting the economic impact of livestock infections. Nature Sustainability, s41893-019-0356-5.
- Probert, W., Jewell, C., Werkman, M., Fonnesbeck, C., Goto, Y., Runge, M., Sekiguchi, S., Shea, K., Keeling, M., Ferrari, M. & Tildesley, M. (2018) Real-time decision-making during emergency disease outbreaks. PLOS Comp Bio 14(7), e1006202.
- Hill, E., House, T., Dhingra, M., Kalpravidh, W., Morzaria, S., Osmani, M., Brum, E., Yamage, M., Prosser, D., Takekawa, J., Xiao, X., Gilbert, M. & Tildesley, M. (2018) The impact of surveillance and control on highly pathogenic avian influenza outbreaks in Dhaka Division, Bangladesh. PLOS Comp Bio 14, e1006439.
BBSRC Strategic Research Priority: Sustainable Agriculture and Food - Animal Health and Welfare.
Techniques that will be undertaken during the project:
The student will use a range of techniques for this project, including landscape epidemiology to develop risk maps for disease spread, 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.
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