Successful management of livestock disease ensures a consistent supply of safe food for everyone in society. The use of data by industry, public services, and even the public at large is a key part of this veterinary public health mission.
However, managing this datacomes with many challenges: how should data collection be standardised, and who should have access to different aspects of this data? What risks are farmers exposed to by making their data publically available? What data and processes need to be maintained to be ableto effectively control shared threats to our supply of safe food within our increasingly international society?
The DigiVet project will study how livestock data is currently used across the partner nations, and how technology, training, and regulatory frameworks might provide societal benefit by improving the public-interest uses of these data. Our study includes workshops with stakeholders to map existing practices, and document the gaps, roadblocks, and opportunities for improvement.
Three case studies will cover foodborne disease, antimicrobial usage, and contagious diseases of livestock. Foodborne illnesses such as Salmonella are a serious public health issue, antimicrobial usage in livestock may be contributing to the developing antimicrobial resistance problem in human pathogens, and exotic and highly contagious livestock diseases such as African swine fever have the potential to devastate our national agricultural sectors, each of which has wide-reaching implications for society as a whole.
Meeting each of these challenges requires different approaches, but each are united by their dependence on similar sources of data to be able for the authorities to continually monitor the threat and act when needed. For each of these applications, we will test the models and statistical data analytics that are used in one partner nation across the other geographic settings, investigating what approaches will work under what circumstances, and what would need to change to facilitate more effective use of the data. We will also investigate the risks associated with missing, sparse, or coarsely aggregated data, and evaluate the potential societal benefits of making better quality data more widely available.
- University of Glasgow (UK)
- University of Edinburgh (UK)
- James Hutton Institute (UK)
- University of Copenhagen (Denmark)
- Danish Veterinary and Food Administration (Denmark)
- National Veterinary Institute (SVA) (Sweden)
- Eesti Maaülikool - Estonian University of Life Sciences (Estonia)
- NVI - Veterinærinstitutet - Norwegian Veterinary Institute (Norway)