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Data-driven control and prioritisation of non-EU-regulated contagious animal diseases

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DECIDE in brief

Farmers, veterinarians and other animal health managers in the livestock and aquaculture sectors are currently missing information on the prevalence and burden of contagious animal diseases that are not regulated by the European Union.

The diseases are estimated to cause 10-15% reduction in performance efficiency of livestock farming, resulting in large financial losses and lower sustainability as well as affect animal welfare.

DECIDE, a five-year Horizon 2020 project, will develop data-driven decision support tools that offer robust and early signals of disease emergence and options for diagnostic confirmation. Moreover, options will be provided for controlling the disease along with their implications in terms of disease spread, economic burden and animal welfare.

This partnership garners expertise in veterinary epidemiology and diagnostics, data science, mechanistic and predictive modelling, economics, animal welfare and social sciences. The consortium can also count on ample access to data from national animal health agencies, providers of veterinary services or farm equipment suppliers. This multidisciplinary consortium brings together 19 partners from 11 European countries. 

  

Upcoming: Collecting stakeholder perspectives on animal health and welfare (Webinar)

November 10, 2022

“Collecting stakeholder perspectives on animal health and welfare: Needs and challenges related to data use analysis visualisations and sharing” – is the title of the VPHI-webinar hosted by Dr. Angela Bearth (ETHZ) next week, Tuesday 15 November 2022. No registration needed. How do the social sciences and veterinary sciences intersect? What methodological approaches are applied



"I will not be satisfied until DECIDE has developed practical tools for livestock farmers and their veterinarians that improve the health and welfare of calves, broilers, piglets and salmon."

Prof. Dr. Gerdien van Schaik
DECIDE Coordinator

This project has received funding from the European Union’s Horizon 2020
research and innovation programme under grant agreement No. 101000494.

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