Fostering Innovation in European Digital Health Surveillance: A User Needs Perspective in Epidemic Intelligence
The global landscape faces an increasing threat from (re)emerging infectious diseases (EIDs) due to factors like climate change and the rising global movement of people and goods. Traditional indicator-based surveillance (IBS) systems have demonstrated vulnerabilities in the early detection, monitoring, and assessment of these EIDs. A significant portion, over 60%, of EIDs are zoonoses, underscoring the critical need for a One Health approach, which currently faces considerable challenges in implementing intersectoral collaborations and integrating diverse data. Within this complex environment, European epidemic intelligence (EI) systems are tasked with processing vast and increasing amounts of information and data concerning disease outbreaks and potential health threats. The sheer quantity and variety of available data sources for EI, alongside the ever-evolving methods for managing and analyzing them, present significant challenges in effective data flow management, analysis, and interpretation.
Recognizing these growing challenges and the imperative to foster innovation in digital health surveillance, a qualitative study by Bouyer et al. aimed to deeply understand the difficulties encountered by EI practitioners across Europe. The core objective was to identify what innovations, from the perspective of these practitioners, could most effectively improve the detection, monitoring, and analysis of disease outbreaks and the emergence of new pathogens. The study specifically sought to describe the current EI activities of European practitioners and their innovation needs within their professional scope, alongside co-designing potential adaptations for new EI tools and services. This research represents a pioneering analysis of innovation requirements in digital health surveillance for EIDs and “Disease X” (as identified by WHO), uniquely considering both the public health (PH) and animal health (AH) sectors throughout Europe.
The methodology involved a qualitative study conducted with 33 EI practitioners from national public health and animal health agencies in five European countries, as well as the European Centre for Disease Prevention and Control (ECDC). A stepwise socio-technical approach was employed, which included identifying stakeholders, understanding their operational problems in EI, and then collectively validating and refining these problems and their most adapted solutions through workshops with practitioners, researchers, and engineers. Data collection utilized various tools such as questionnaires, semi-structured interview guides, and a digital tool for idea exchange (Klaxoon®), ensuring an iterative process grounded in the practitioners’ operational objectives and real-world problems.
The findings highlighted several critical needs: practitioners expressed a desire to collectively review their EI strategy to bolster preparedness for emerging infectious diseases, adapt their routines to manage increasing data volumes, and receive methodological support for conducting cross-sectoral analyses. Key demands included timely, validated, and standardized data acquisition processes through methods like text mining of various sources, along with better validated dataflows that respect data protection rules. They also emphasized the need for more interoperable data with consistent quality levels and standardized covariate sets for national epidemiological assessments. While identifying solutions such as visualization, text mining, and predefined analytical tools combined with methodological guidance, practitioners explicitly favored partial rather than full automation of analyses. This preference stems from a desire to maintain control over data and inputs, allowing for adaptation of parameters to versatile objectives and characteristics. The study concludes that the required solutions must be built upon holistic and integrated approaches for monitoring zoonoses and antimicrobial resistance, fostering harmonization across agencies and sectors while retaining flexibility in tool and method selection.
Reference for this article:
Bouyer, F., Thiongane, O., Hobeika, A., Arsevska, E., Binot, A., Corrèges, D., Dub, T., Mäkelä, H., van Kleef, E., Jori, F., Lancelot, R., Mercier, A., Fagandini, F., Valentin, S., Van Bortel, W., & Ruault, C. (2024). Epidemic intelligence in Europe: a user needs perspective to foster innovation in digital health surveillance. BMC Public Health, 24(1), 973. https://doi.org/10.1186/s12889-024-18466-1
