The urgency of addressing zoonotic diseases surveillance: Potential opportunities considering One Health approaches and common European Data Spaces

TL;DR

This paper discusses how better data sharing in Europe could improve tracking of zoonotic diseases and help prevent future health crises.

Contribution

The paper proposes using the European Data Spaces framework to enhance animal surveillance data integration for One Health approaches.

Findings

Current transdisciplinary animal surveillance data are insufficient for anticipating public health threats.

The European Data Spaces framework could enable better integration of data sources for zoonotic disease preparedness.

An overarching data layer could support data sharing and quality-controlled access for researchers.

Abstract

Currently, transdisciplinary data from animal surveillance that are available for One Health approaches to public health are scarce, negatively impacting our ability to anticipate and prepare for future public health threats, particularly those involving zoonotic diseases with pandemic or epidemic potential. In this article, we explore the potential of the common European Data Spaces framework to enhance the availability of animal surveillance data, in order to better address public health threats. We propose building upon and expanding existing initiatives, such as the European Data Spaces for Health, Agriculture, and Green Deal, to design innovative services. These services could enable the integration of different data sources to inform research and policymaking on public health interventions. An overarching layer, populated with data and generating integrative information, could support a One Health approach to research and policymaking for the preparedness and anticipation of zoonotic diseases. Consequently, this approach might foster data sharing from Member States by leveraging existing developments within data spaces in terms of, for example, data security. It could also support researchers and developers in accessing transdisciplinary, stratified, and quality-controlled data for their projects.