Epidemic intelligence in Europe: A user needs perspective to foster innovation in digital health surveillance

Bouyer Fanny, Thiongane Oumy, Hobeika Alexandre, Arsevska Elena, Binot Aurélie, Corrèges Déborah, Dub Timothée, Mäkelä Henna, Van Kleef Esther, Jori Ferran, Lancelot Renaud, Mercier Alizé, Fagandini Ruiz Francesca, Valentin Sarah, Van Bortel Wim, Ruault Claire. 2024. Epidemic intelligence in Europe: A user needs perspective to foster innovation in digital health surveillance. BMC Public Health, 24:973, 20 p.

https://doi.org/10.1186/s12889-024-18466-1

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Résumé : Background: European epidemic intelligence (EI) systems receive vast amounts of information and data on disease outbreaks and potential health threats. The quantity and variety of available data sources for EI, as well as the available methods to manage and analyse these data sources, are constantly increasing. Our aim was to identify the difficulties encountered in this context and which innovations, according to EI practitioners, could improve the detection, monitoring and analysis of disease outbreaks and the emergence of new pathogens. Methods: We conducted a qualitative study to identify the need for innovation expressed by 33 EI practitioners of national public health and animal health agencies in five European countries and at the European Centre for Disease Prevention and Control (ECDC). We adopted a stepwise approach to identify the EI stakeholders, to understand the problems they faced concerning their EI activities, and to validate and further define with practitioners the problems to address and the most adapted solutions to their work conditions. We characterized their EI activities, professional logics, and desired changes in their activities using NvivoⓇ software. Results: Our analysis highlights that EI practitioners wished to collectively review their EI strategy to enhance their preparedness for emerging infectious diseases, adapt their routines to manage an increasing amount of data and have methodological support for cross-sectoral analysis. Practitioners were in demand of timely, validated and standardized data acquisition processes by text mining of various sources; better validated dataflows respecting the data protection rules; and more interoperable data with homogeneous quality levels and standardized covariate sets for epidemiological assessments of national EI. The set of solutions identified to facilitate risk detection and risk assessment included visualization, text mining, and predefined analytical tools combined with methodological guidance. Practitioners also highlighted their preference for partial rather than full automation of analyses to maintain control over the data and inputs and to adapt parameters to versatile objectives and characteristics. Conclusions: The study showed that the set of solutions needed by practitioners had to be based on holistic and integrated approaches for monitoring zoonosis and antimicrobial resistance and on harmonization between agencies and sectors while maintaining flexibility in the choice of tools and methods. The technical requirements should be defined in detail by iterative exchanges with EI practitioners and decision-makers.

Mots-clés Agrovoc : surveillance épidémiologique, système d'aide à la décision, épidémiologie, évaluation du risque, covid-19, santé animale, analyse du risque, intelligence artificielle, fouille de textes

Mots-clés géographiques Agrovoc : Italie, France, Espagne, Serbie, Finlande, Europe

Mots-clés libres : Zoonotic diseases, One Health, Digital tools, Event-based surveillance, Big data, Sociology of innovation, Sociology of work, Co-design, Co-creation, Co-conception

Classification Agris : S50 - Santé humaine
L73 - Maladies des animaux
L70 - Sciences et hygiène vétérinaires - Considérations générales

Champ stratégique Cirad : CTS 4 (2019-) - Santé des plantes, des animaux et des écosystèmes

Agences de financement européennes : European Commission

Programme de financement européen : H2020

Projets sur financement : (EU) MOnitoring Outbreak events for Disease surveillance in a data science context

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