Answering key questions of One Health antimicrobial resistance using a new livestock research data platform
The project: Antimicrobial resistance (AMR) is becoming an increasingly important topic worldwide, and veterinary medicines use - particularly in the agricultural sector - has come under sustained scrutiny. Growing political pressure to reform and reduce antimicrobial use in order to stem the tide of AMR continues to build, although methods of collecting and analysing data are far behind demand.
Through interdisciplinary collaborations, we are creating a surveillance system and research data platform for UK-wide recording of medicines use, disease prevalence and AMR in the cattle sector, as well as elucidating its relationship with companion animal and human resistance. This initiative has generated substantial buy-in from government and the livestock and food industries. We have already gained access to the complete veterinary records and clinical notes of the UK’s largest commercial livestock veterinary practices, have extracted data from practice management systems of >50% of all cattle in the UK and have included laboratory diagnostics as well as antibiotic susceptibility for thousands of isolates. Up to now the challenge has been to harmonise and link this data, which has been achieved by a current PhD studentship on building surveillance data infrastructure.
The objective of this studentship is to exploit these data through development and application of machine learning, statistical data science and infection modelling approaches to extract new biological understanding about the development and transmission of AMR. Challenges here include, through industry engagement and supervision, the rationalisation of stakeholder requirements with the inferential capacity of the databank. Timely questions of ownership and sharing of data between farmers, veterinarians, the industry and government will be explored using social science techniques which will help inform all stakeholders currently interested in and using these types of data. Good governance practices will be established and communicated widely. Validated methods will then be incorporated back into our real-time surveillance platform, together with dashboards and visualisations for farmers, veterinarians and industry which will be co-created alongside these stakeholders.
This studentship will start in September 2020.
How to apply:
This studentship is part of the BBSRC SWBio Doctoral Training Partnership (https://www.swbio.ac.uk/). For UK and EU students satisfying the eligibility criteria (https://www.swbio.ac.uk/programme/eligibility/), please apply directly at https://www.swbio.ac.uk/programme/projects-available/. For International students and others outside this eligibility criteria, we are keen to accept students onto the programme who are self-funded or will apply for a scholarship scheme such as the China Scholarship Council (http://www.bristol.ac.uk/fees-funding/awards/china-scholarship-council/) or those from other countries (see http://www.bristol.ac.uk/fees-funding/search/ for a list). In the first instance, please contact us if you intend to follow this path.
Please see https://www.swbio.ac.uk/programme/eligibility/.
Standard University of Bristol eligibility rules apply. Please see http://www.bristol.ac.uk/study/postgraduate/2019/health-sciences/phd-veterinary-sciences/
Contacts: Kristen Reyher ([Email Address Removed]), Andrew Dowsey ([Email Address Removed])
Funding: For eligible students, funding is available for full UK/EU tuition fees as well as a Doctoral Stipend matching the UK Research Council rate (e.g. £14,777 for 2018/19, updated each year) for 4 years. An enhanced stipend is available for eligible students with a recognised veterinary degree qualification (£22,456 per annum). Research training costs are included, as are additional funds to support conferences and a 3-month industrial internship.
How good is research at University of Bristol in Agriculture, Veterinary and Food Science?
FTE Category A staff submitted: 34.03
Research output data provided by the Research Excellence Framework (REF)
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