Istituto Zooprofilattico Sperimentale
IZSPLV is a state organisational Institute providing services to enforce EU agri-food chain.
Conception and operation involve and require highly skilled staff, unique technological developments and data management systems offering opportunities for health safety improvement, science innovation and technological development and can attract investments and contribute broadly to the socio-economic advance.
The main activities embrace the entire food chain, and particularly experimental research on aetiology and pathogenesis of infectious and spreading animal diseases, hygiene of breeding and livestock productions. Essays and bioassays for laboratory diagnosis of animal diseases, microbiological and chemical safety of food and zoo-technical feed, epidemiology monitoring on the field of animal health, livestock hygiene and food of animal origin, the production of vaccines, reagents and immunological products for the prophylaxis and the diagnosis of animal diseases, training and refreshing of veterinaries and other operators in the Veterinary Public Health.
IZSPLV has the availability and complexity of data has led to new opportunities and challenges in veterinary epidemiology around how to translate abundant, diverse, and rapidly growing “big” data into meaningful insights for animal health. Big data analytics are used to understand health risks and minimize the impact of adverse animal health issues through identifying high-risk populations, combining data or processes acting at multiple scales through epidemiological modeling approaches, and harnessing high velocity data to monitor animal health trends and detect emerging health threats. The advent of big data requires the integration of new skills and new trends into veterinary epidemiology training, including, for example, machine learning and coding, to prepare a new generation of scientists and practitioners to engage with big data. Establishing pipelines to analyze big data in near real-time is the next step for progressing from simply having “big data” to create “smart data,” with the objective of improving understanding of health risks, effectiveness of management and policy decisions, and ultimately preventing or at least minimizing the impact of adverse animal health issues.