Atlantic salmon (Salmo salar) is a globally significant food and protein source, with over 2.2 million tonnes produced annually worldwide. However, in recent years, Atlantic salmon supply has fluctuated, partly as a result of infectious disease outbreaks in all major salmon producing countries (FAO 2017). Salmon farming in Chile generates a substantial proportion of the world salmon production, with over 700,000 tonnes of marine finfish produced annually. In Chile, Salmon Rickettsial Syndrome (SRS) has had a substantial impact, responsible for an estimated 74% of salmon aquaculture mortalities, costing the industry up to $100m each year (Correa et al. 2015). An emerging outbreak of complex gill disease is also causing substantial and rapidly increasing losses. These challenges are illustrative of disease threats faced right across the aquaculture sector and essential food supply. The goal of this project is to develop statistical and modelling tools for disease dynamics to help address current gaps in knowledge and practical know-how in order to tackle such challenges.
Current limitations exist in both the biological knowledge of the pathogen dynamics, as well as the application of quantitative methods. In the context of European aquaculture production, our research group has developed a set of modelling and statistical techniques which address key disease threats, particularly Amoebic Gill disease. These combine: (i) detailed simulation of disease transmission and progression through populations; (ii) data from experimental or field outbreaks; and (iii) computational statistics approaches to rigorously estimate from such data, key parameters that are typically difficult or not currently possible to measure directly. Whilst aquaculture is an industry producing substantial amounts of data, there are a range of technical barriers to extracting meaningful information. Detailed modelling and purpose built computational statistical approaches provide methods to mitigate this as well as tools with which to inform future data collection and build operational disease control systems.
This project will further develop these methods with the aim of characterising the epidemiology of key pathogens and building frameworks for the assessment of disease management and control, for eventual deployment in field decision support. Building detailed models of transmission and associated risk factors within populations provides a tool for exploring disease dynamics under specific control scenarios and different management parameters.
Working with Landcatch, a company with multinational reach and extensive expertise in Salmon, the project will seek to combine production and experimental challenge data, with a focus on applicability under field conditions. Our group has developed and refined a number of Bayesian statistical approaches specifically for disease transmission models. Combined, the modelling and statistical approaches will provide a framework to assess a range of management approaches in terms of the cost implications, both from implementing the measures and the potential mitigated disease impact.
This studentship will require broad cross disciplinary working and will develop the skills accordingly, from high performance informatics, statistics and biology to working with industry. The training will encompass a number of cutting edge statistical methods, as well as population level epidemiological modelling and practical modelling and statistics applied in an industry setting. The student will be expected to engage with the aquaculture industry in the UK, Chile and internationally.
Applicants should download the required forms from http://www.eastscotbiodtp.ac.uk/how-apply-0
and send the following documents to [email protected]
a. EASTBIO Application Form
b. EASTBIO DTP Equality Form
d. Academic transcripts (a minimum of an upper second class or first class honours degree or equivalent is required for PhD study
e. Two references should be provided by the deadline using the EASTBIO reference form (http://www.eastscotbiodtp.ac.uk/how-apply-0
). Please advise your referees to return the reference form to [email protected]
f. If you are nominated by the supervisor(s) of the EASTBIO PhD project you wish to apply for, they will provide a Supervisor Support Statement.