Georgina is a chartered information professional who graduated from City University with a Master’s degree in Information Science in 2004. Her information management career spans higher education, financial services, and technology sectors.
As part of the Veterinary Health Innovation Engine (vHive) team, Georgina laid the foundations of the data governance strategy, data gathering efforts including the addition of social listening functionality and ontology development to aid the discoverability of data and interoperability with external systems, data management and data visualisation aspects of the Data Innovation Hub for Animal Health (DIHAH). DIHAH facilitates data sharing in the animal health sector and provides the no-code tools to enable actionable insights to be derived from combined data.
vHive is a research centre, startup, and incubator supported by a co-investment of £8.5 million in resources dedicated to the development and adoption of new digital technologies in animal health. vHive has been involved in a number of projects including the African Livestock Productivity and Health Advancement (ALPHA) Initiative, a joint project funded by the Bill and Melinda Gates Foundation and Zoetis, for which Georgina has worked closely with the researchers to develop data management plans and processes for long-term data storage and reuse.
Areas of specialism
Affiliations and memberships
The Veterinary Health Innovation Engine (vHive) aims to bridge the technology readiness level gap between the world’s leaders in animal health and to lead the development and application of transformational digital and data analytics tools to advance the wellbeing of animals for the benefit of society. Working with partners in industry and academia, vHive’s focal research areas include platform and app creation, business insights, project management, data storage, and research validation. As Data Scientist, Georgina specialises in data governance and management; data collection and analysis; and the development of information structures including taxonomies and ontologies.
Since joining the platform and app creation team at vHive, Georgina has been engaged in the development of the Data Innovation Hub for Animal Health. DIHAH facilitates data sharing and enables users to derive actionable insights from harmonising and visualising animal health data. Georgina has led the development of several data visualisation dashboards for proof of concept and demonstration purposes; created data governance processes; enabled the construction of secure data storage infrastructure; built a data catalogue using open data sources and constructed taxonomies and ontologies to aid discoverability of data on the platform.
Georgina is an active participant in vHive projects including the African Livestock Productivity and Health Advancement (ALPHA) Initiative; a three-year partnership between vHive and Zoetis, co-funded by the Bill & Melinda Gates Foundation and Zoetis. The ALPHA Initiative ran from 2017-2021 and the aim was to deliver sustainable enhancement of livestock health and production in sub-Saharan Africa (SSA), through increased availability of veterinary medicines, enhancing veterinary diagnostics and laboratory networks, and providing a platform for education, training, and information sharing in Uganda, Tanzania, Ethiopia, and Nigeria. Georgina is working with the ALPHA team to develop long-term data management processes for data storage and reuse.
The health of animals and people are interconnected and interdependent, a fact increasingly recognised by global organisations concerned with human and animal health. Sixty percent of infectious diseases in humans are zoonotic and yet these diseases and their consequences are poorly characterised. Of additional concern is antimicrobial resistance (AMR) and its relationship to antibiotic use in food animals. Agricultural and animal health workers are directly at risk of colonisation with drug-resistant bacteria through close contact with infected animals providing a conduit for the entry of resistance genes into community and hospital environments, where further spread into pathogens is possible. This One Health study will address urgent gaps in understanding the impact of zoonotic disease on human health and healthcare resource use; and the association between occupational exposure to food animals and antimicrobial treatment patterns, antibiotic treatment failure, and reported AMR. The study will utilise Clinical Practice Research Datalink (CPRD) anonymised patient-level human data from UK general practice and linked hospital encounters, death registration, census-based deprivation and rurality data.
Good ranch management practice is essential if optimal livestock production, productivity and marketing are to be realised. Ranch owners in Uganda are looking to maximise returns on the investment made and ongoing operating costs being incurred. The project aim is to provide ranch owners, particularly absentee owners, with a data driven menu of actions that will guide them on the priority actions needed and how these can be monitored, and impact assessed. vHive will lead on data management aspects of the project and Zoetis and Makere University collaborators will advise on farm management and equipment. The team will collect, analyse, and report on livestock productivity data and make these available to the ranch owner.
The open data market size is estimated at €184 billion and forecast to reach between €199.51 and €334.21 billion in 2025. In this paper, we conceptualise the semantic data innovation platform, which will be able to answer inter-disciplinary questions via semantic reasoning over open data. We use 750 open animal healthcare datasets to exemplify this work, covering mainly poultry, swine, ruminants, and other livestock, which are complemented by open data from complementary domains, such as geographic location, medicine and virology. We aggregate the domain knowledge (classes) and enable the logical links (properties) between these classes. The prototype encapsulates the complexity of animal healthcare knowledge into ontology, which can answer complex questions using semantic reasoning on the datasets (answer-as-a-service).
An owner's ability to detect changes in the behavior of a dog afflicted with osteoarthritis (OA) may be a barrier to presentation, clinical diagnosis and initiation of treatment. Management of OA also relies upon an owner's ability to accurately monitor improvement following a trial period of pain relief. The changes in behavior that are associated with the onset and relief of pain from OA can be assessed to determine the dog's health-related quality of life (HRQOL). HRQOL assessments are widely used in human medicine and if developed correctly can be used in the monitoring of disease and in clinical trials. This study followed established guidelines to construct a conceptual framework of indicators of HRQOL in dogs with OA. This generated items that can be used to develop a HRQOL assessment tool specific to dogs with OA. A systematic review was conducted using Web of Science, PubMed and Scopus with search terms related to indicators of HRQOL in dogs with osteoarthritis. Eligibility and quality assessment criteria were applied. Data were extracted from eligible studies using a comprehensive data charting table. Resulting domains and items were assessed at a half-day workshop attended by experts in canine osteoarthritis and quality of life. Domains and their interactions were finalized and a visual representation of the conceptual framework was produced. A total of 1,264 unique articles were generated in the database searches and assessed for inclusion. Of these, 21 progressed to data extraction. After combining synonyms, 47 unique items were categorized across six domains. Review of the six domains by the expert panel resulted in their reduction to four: physical appearance, capability, behavior, and mood. All four categories were deemed to be influenced by pain from osteoarthritis. Capability, mood, and behavior were all hypothesized to impact on each other while physical appearance was impacted by, but did not impact upon, the other domains. The framework has potential application to inform the development of valid and reliable instruments to operationalize measurement of HRQOL in canine OA for use in general veterinary practice to guide OA management decisions and in clinical studies to evaluate treatment outcomes.