Data governance and ethics in digital health surveillance for emerging infectious diseases

Abstract

Abstract Epidemic intelligence, and in particular, its component of digital health surveillance, combines multiple large, heterogeneous datasets, often by using artificial intelligence (AI) systems to detect, monitor, and assess threats relevant to public and animal health. This could raise significant ethical issues regarding data sources, natural language processing, user privacy and consent, among others. The European Commission is highly engaged in how European projects using AI for health data and digital health surveillance comply with the General Data Protection Regulation and ethical principles. This work aimed to better understand the governance of data in the H2020 MOOD (Monitoring Outbreak for Disease Surveillance in Data Science Context) project. The authors also studied the perceptions and views of researchers on ethical risks and suggested actions to mitigate these risks in an international multisource Big Data Analytics and One Health project. First, a data mapping approach was used to determine the origin and destination of the data in the project. Participatory observations were conducted to understand the data scientists at work. Information was also collected through a qualitative study using semi-structured interviews with eight project researchers ranging from data scientists to epidemiologists and ethics experts; a quantitative survey of all consortium members complemented this process. Big data and AI systems have enormous potential for strengthening healthcare delivery, including deploying different public health interventions such as disease surveillance, outbreak response and health system management. However, some risks and constraints could hamper the reliability of data analysis and AI systems, such as the deidentification, lack of privacy, compliance with Twitter Application Programming Interfaces terms of use, and the risk of reproducing bias and stigmatisation of minorities. Our findings suggest that few researchers could be reluctant to work and establish action to mitigate ethical risk depending on the approach used in ethical counselling for European and transdisciplinary projects. The philosophical and comprehensive approach to ethics is judged softer when comparing the legal and more constraining requirements to comply with the law. Using Big, multisource EI data in a One Health framework requires consideration of strong ethical principles that safeguard users’ privacy and constant ethical support for researchers.