Epidemic Intelligence in Europe: a users’ needs perspective to foster innovation in digital health surveillance

Abstract

Abstract 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 analyze these data sources are constantly increasing. Our aim was which innovations, according to EI practitioners, could improve the detection, monitoring and analysis of disease outbreaks and emergence of new pathogens. Methods We conducted a qualitative study to identify the needs for innovation expressed by 33 EI practitioners of national public health and animal health agencies in five European countries and at the European Center for Disease Control (ECDC). We adopted a step-wise approach to identify the EI stakeholders, to understand the problems they faced concerning their EI activities, to validate and further define practitioners’ problems to offer solutions. We characterized their EI activities, their professional logics, and the desired changes in their activities. Results Our analysis highlights that EI practitioners wished to collectively review their EI strategy in order to enhance their preparedness for emerging infectious diseases, adapt their routine to manage an increasing amount of data and to 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 covariates sets for epidemiological assessments of national EI. The set of solutions, identified to facilitate risk detection and risk assessment, included visualization, text-mining, predefined analytical tools combined with methodological guidance. Practitioners also highlighted their preference for a partial rather than a full automation of analyses in order to keep control over the data, inputs and adapt parameters to versatile objectives and characteristics. Conclusions Overall, the set of solutions had to be based on holistic and integrated approaches for monitoring zoonosis and antimicrobial resistance and on a harmonization between agencies and sectors, while keeping 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.