Modelling the spread of disease in honey bees, and predicting the efficacy of control methods.

Develop 2D spatio-temporal deterministic models of common honey-bee diseases and pathogens to understand how they can be better controlled and eliminated. A fundamental ecological question is how the spatial scale of infection influences eradication. We will look to use data from the UK and other islands to address the question of when invaders can be eliminated.
This will be done through the use of models matched to data through MCMC methods.
For example, Datta et al 2013 (Modelling the spread of American foulbrood in honeybees) used a 2D spatial model to model spread of disease in Jersey which also found links between apiaries owned by the same beekeeper. This information can then be used to identify how control methods will impact the system. This is an example of the kind of research I would like to do.
Bee disease is critical in the real world because bees are an important part of global agriculture. The National Bee Unit supports research into bee disease and care across England.
Collaborations in other countries and regions will also be sought during the project.
The context of the research - Bee disease is critical in the real world because honey bees are an important part of global agriculture in terms of pollination services. For example, Varroa Destructor has not yet invaded Australia, and therefore understanding how varroa spreads (whether transmission is mostly hive to hive, or between hives owned by the same keeper) may allow for control methods to be implemented more efficiently.
The aims and objectives of the research -
1) Development of spatio-temporal models for the spread of disease in bees, and prediction of control methods efficacy.
2) Matching models against data for a number of case studies of invasion.
3) Develop an understanding of invasion and control, and how this is affected by segregation of the landscape.
The novelty of the research methodology - There are very few detailed models of disease spread in honey bees. The generic question about spatial scales and control of invasive pest is poorly studied, and there's also a lack of good methodology. Apart from foot and mouth disease, there is a general lack of modelling of commercial live stock diseases. Furthermore, bee keeping is generally unlicensed and unregulated, so there is a lack of high quality data. This is also due to the lack of interest from government bodies.
The potential impact, applications, and benefits - My research could be used to predict the spread of disease in managed honey bee populations. It may suggest better methods of targeted control, or may highlight when control is impossible.
How the research relates to the remit - This research falls into EPSRC Themes of "Living with Environmental Change", "Global Uncertainties" and "Mathematical Sciences" - using mathematical models to consider invasion in managed populations.
It overlaps with EPRSC Research Areas: Complexity science, Mathematical Biology, Nonlinear systems, Statistics and applied probability.
Research area; Global uncertainties, LWEC [Living With Environmental Change], Mathematical Sciences
External Partner - National Institute of Water and Atmospheric Research

Impact from the MathSys CDT will arise from three separate mechanisms, each of which will generate a spectrum of academic, economic and societal impacts.

1) Most prominently, this CDT will create the next generation of quantitative researchers that are trained in the necessary skills and techniques to make substantial impact in academia, industry and government agencies. Creation of skilled researchers with a broad scientific outlook will have a number of beneficiaries. We expect that our students will be in high demand within academia and will be the researcher leaders of tomorrow. In addition, many of our brightest students post-PhD are now moving out of academia to research positions within industry or government agencies; such students are likely to generate substantial financial impact within industry and societal benefits within government agencies. By encouraging strong collaboration with our external partner organisations throughout their training, our PhD students will have a broad insight into the impact that mathematics can bring, and the routes through which academic excellence can be translated into meaningful applied outputs with impact. The assembled team of supervisors has an excellent track-record of supporting and training high calibre PhD students with skills that are in demand both within and outside of academia.

2) More immediate economic and societal benefits will accrue from the direct interaction of our students with external partners that is an integral part of their training. We anticipate that 4-6 students per cohort will undertake a PhD that is co-supervised by one of our external partner organisations; in addition all students during their MSc year will partake in one of several group projects led and supported by one of our external partners. In both cases, research will be focused towards real-world problems that are of current concern to the partners. It is anticipated that through these close interactions our students will develop methodologies and results that will address real-world problems. These new solutions to particular challenging real-world problems from external partners are likely to have substantial industrial, economic or societal benefits as they directly tackle prominent and pressing issues set by those with the greatest knowledge of the real-world challenges. Impact will therefore be generated through direct problem-solving research with a number of the UK's leading organisations.

3) Finally, we envisage that the mathematical techniques that are developed in the context of one real-world problem will have wider benefit to other academic fields. Although the immediate beneficiaries are likely to be other academics who will gain from an increased repertoire of tools and techniques, in the longer term these insights are likely to lead to new applications that feed back into industry, finance and society in general. The transdisciplinary nature of our MathSys CDT will facilitate such interactions, promoting the exchange of ideas between diverse subject areas. We firmly believe that such cross-fertilisation of ideas will be a feature of the MathSys CDT, where students are united by common goals of quantitative understanding and prediction and a common language of mathematics. We therefore expect rapid impact in a variety of applied areas, as novel techniques are introduced.