DECIDE

Despite the impact of non-EU-regulated animal diseases, there is currently a lack of knowledge on the prevalence and the burden of these diseases. Private stakeholders in the livestock sector all over Europe lack adequate tools for risk assessment and prioritization of disease control measures. As leaders of the Work Package 3, the SVA team will develop tools that will be the bridge between the data-driven models and the actual process of decision making.

OUR EXPERTISE

At start, four dedicated researchers with experience in surveillance design and evaluation, risk assessment, and epidemiological research work in the project. As the project evolves, SVA will provide advice in the management of disease suspicions and outbreak investigations.

• Jenny Frössling
  Epidemiologist, Associate Professor. Link to personal page.
• Fernanda Dórea
  Epidemiologist, veterinarian, PhD. Link to personal page. 

Fernanda is a veterinary epidemiologist from Brazil, with experience with animal disease surveillance in 4 countries. Her research is focused on the extraction of actionable information from animal health data (data-driven surveillance), including areas such as data interoperability, knowledge modeling, data digitalization workflows and data visualization to support decision making.

• Anna Ordell
  Epidemiologist, veterinarian.

Anna is a veterinary epidemiologist with a background as animal health veterinarian, and advisor in beef and dairy farms for several years.

• Gema Vidal
  Epidemologist, veterinarian, PhD.  Link to personal page.

Gema has more than ten years of experience as livestock veterinarian and as researcher. Since 2002, she has been combining clincal activities with applied research in Spain, U.S.A., Nicaragua, and Ethiopia. Gema completed her clinical training programs as University of Florida and University of California Davis, and holds a Masters in Preventive Veterinary Medicine (MPVM) and a PhD in Epidemiology, both from UC Davis. As researchers, she focuses on the early detection of syndromes by using a combination of statistical and machine learning methodologies, with emphasis on the intergration of diverse data sources.

THE DECIDE PROJECT

The DECIDE project consists of seven work packages that cover across all the different areas needed to develop decision support tools and workflows so these can be used by different users.  Click here for more information on the project.