Title: NordForsk Digitalisation of the Public Sector: Digitalisation of livestock data to improve veterinary public health
Funder: Economic and Social Research Council

Abstract: This project will use examples from three case studies in order to achieve four objectives: 1. Identify and compare enabling factors for data digitalisation, and the data ecosystems that have emerged in different countries for the purposes of animal health contingency planning. This involves exploring the regulatory frameworks and social factors and will be co-produced with key stakeholders to explore aspirations and goals for future digitalisaiton of livestock-related data. 2. Explore existing and emerging digital innovations for collecting, managing, using, and preserving these data (over different timescales), and use them to develop and deliver publicly available workflows that promote data FAIRness and support data transformation into actionable information for animal disease preparedness and control. Consideration of data rights and resilience to system shocks (sucha as weakened infrastructures and institutions, and scarcening resources) will be key. 3. Assess societal and end-user perspectives and impacts by identifying best practices from these case studies and discussing their implications in open dialogue with industry and government stakeholders. We will also explore the long-term effects of transforming livestock-related data for both stakeholders and end users, and how future challenges can best be addressed. 4. Summarize the lessons learned within each case study, suggesting best practices and producing guidelines evidencing the challenges and further opportunities for digitalisation of livestock data.

Lay abstract: 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 data comes 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 able to 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.

Research activity codes: 3.2 INTERVENTIONS TO ALTER PHYSICAL AND BIOLOGICAL ENVIRONMENTAL RISKS
Health categories: INFECTION