About the Project
Livestock movements are an important mechanism of infectious disease transmission. Understanding the complexity of the network formed by these movements is critical to predict the spread of diseases, assess the benefit of prevention and control measures and design cost efficient surveillance programmes. However, other mechanisms and relationships may connect farms to each other and represent additional routes of infection. Not accounting for these additional routes will lead us to underestimate the role of individual farms and limit the benefit of targeted strategies to control and prevent infectious diseases.
Multilayer networks, where each layer of connectivity may relate to one type of relationship, can be used to represent many complex systems more accurately than was previously possible. Particularly, multilayer networks help us to consider the different relationships that occur within and between layers. These respective intra-layer and inter-layer edges between actors of the networks are fundamentally different, and may impact differentially on the physical functionality of the network. This complexity has led to an explosion of work on the physics of multilayer networks as well as applications in computer science and ecology. However, it remains in its infancy in epidemiology.
On-going research being carried out in EPIC, Scotland’s Centre for Expertise in Animal Disease Outbreaks, is evaluating the role of different multilayer structures arising from contact relationships between farms in the dynamics of infectious swine diseases in Great Britain (GB). Using large volume of data, independent, directed and weighted multilayer networks have been constructed between all pig farms in GB (nodes) where relationships between farms (edges) include animal movements and the use of private transporters (potential layers). This work has provided insights on the role of transportation in increasing the connectivity between farms and its impact on disease spread but also highlighted an important challenge: what is the implication of inter-layer edges in the dynamics of infectious diseases.
In this project, you will further investigate the use of multilayer networks in animal health, better characterise the temporal dynamics of multilayer networks and explore the role of inter-layer edges on the spread of infectious diseases. You will use the large volume of data available to develop and evaluate algorithms for identifying farms with higher risk of onward disease spread or higher susceptibility to be targeted for disease surveillance and control programmes. The work will feed into research being conducted within EPIC and support its research programme on infectious swine diseases [1-3].
This PhD project will be undertaken within The Roslin Institute, in collaboration with Biomathematics and Statistics Scotland (BioSS). Beside working with world-leading research groups in livestock disease epidemiology, modelling and statistics, this project has the potential to directly influence policy in Scotland and in the UK and inform decisions of industry stakeholders.
The student will have access to comprehensive training in both epidemiology and in mathematical modelling as well as broader training in computer science/maths & statistics if required.
All candidates should have or expect to have a minimum of an appropriate upper 2nd class degree. To qualify for full funding students must be UK or EU citizens who have been resident in the UK for 3 years prior to commencement.
Completed application form along with your supporting documents should be sent to our PGR student team at [Email Address Removed]
Please send the reference request form to two referees. Completed forms for University of Edinburgh, Royal (Dick) School of Veterinary Studies and the Roslin Institute project should be returned to [Email Address Removed] by the closing date: 5th January 2020.
It is your responsibility to ensure that references are provided by the specified deadline.
Download application and reference forms via:
 Data-driven risk assessment from small scale epidemics: Estimation and model choice for spatio-temporal data with application to a classical swine fever outbreak. Gamado et al Frontiers in Veterinary Sciences 4:16 (2017). https://doi.org/10.3389/fvets.2017.00016
 Vulnerability of the British swine industry to classical swine fever. Porphyre et al, Scientific reports 7:42992 (2017). https://www.nature.com/articles/srep42992
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