Towards eradication of the commonest UK foodborne pathogen: mathematical modelling and data analysis

Campylobacteriosis is the commonest food-borne zoonosis in the UK with 700,000+ cases per annum. Food-borne illness exerts a recurring cost to the UK economy of >£2bn/annum, and control of Campylobacters is a key target for eradicating this burden. Chicken products are the main source of human campylobacteriosis in the UK. The bacteria can easily spread through poultry flocks within a single farm or across the entire country by transport or wild birds. A promising strategy for reducing the levels of this bacterial pathogen or even eliminating it in chickens is phage therapy. However, implementation of phage therapy may easily fail if it is not done in a proper scientific way. Mathematical modelling of bacteria-phage interaction would be of great help to optimize the phage therapy and reduce the number of cases.

The main goal of the PhD project is to develop a set of predictive mathematical models to describe phage-bacteria interactions in chickens (in a single bird, within a farm and at the level of a region) as well as to provide recommendations on the efficient disease control and pathogen elimination. This project will be focused on modelling of how the development of resistance of bacteria to phages will affect the pathogen loads and spread as well as how such resistance would influence the pathogen control strategy in the UK. Mathematical modelling and computation will be combined with analysis of data sets obtained by researchers from the Department of Genetics at the University of Leicester.

Methodology

The PhD student will develop mathematical models of bacteria-pages dynamics using the existing data sets. To be able to easily work with data, he/she will receive an extensive training from data mining experts of the Department of Mathematics. The student will firstly develop a simple model to describe bacteria-page dynamics within an individual bird. Then evolution of bacterial resistance will be added using stochastic simulations. Interactions within a group of birds will be described via an adaptive network model represented by a stochastic graph transformation to account for evolving networking structure among birds inside a single farm as well as a permanently changing network structure of links between connected farms in the UK. Initial networks models will be reduced into mean-field ODEs models.

This will be done using a new algorithm developed by the Department of Informatics (Leicester) and the student will receive an appropriate training (no preliminary knowledge of the topic is required!). The obtained mean-field ODEs models will be studied in depth using bifurcation analysis. Finally, applying methods of optimal and feedback controls, an optimal timing, the strength and the number of vaccination treatments of chickens will be suggested at the level of a farm as well at the level of the whole country.

With continual support in mathematics, informatics and biology from experts in the departments of Mathematics, Informatics and Genetics, this student will build up a truly interdisciplinary research profile.