Cybersecurity Gaps in Digital Epidemiology: Safeguarding Medical Surveillance in the Age of AI and Global Pandemics

Abstract

Digital epidemiology leverages real-time data and artificial intelligence (AI) to monitor and predict disease trends. However, the growing integration of public health surveillance and digital technology introduces significant cybersecurity vulnerabilities. This review critically examines the current gaps in cybersecurity within digital epidemiology, emphasizing threats from AI-driven analytics, regulatory fragmentation, and challenges disproportionately affecting low- and middle-income countries (LMICs). To guide mitigation strategies, we propose a layered socio-technical framework comprising three interconnected domains: (1) technological safeguards (e.g., secure AI architectures and data encryption), (2) ethical and governance mechanisms (e.g., consent, transparency, surveillance accountability), and (3) legal and institutional coordination (e.g., harmonized international regulations and LMIC capacity building). By applying this framework, we evaluate current practices and outline integrative recommendations to enhance resilience, equity, and trust in digital disease surveillance systems.