Infectious Disease Surveillance in the Era of Big Data and AI: Opportunities and Pitfalls

Abstract

The landscape of infectious disease surveillance (IDS) is undergoing a profound shift, driven by the rapid emergence of big data and artificial intelligence (AI). Traditional surveillance systems, while foundational to public health, are increasingly limited by delayed reporting, data silos, and fragmented information flows. In response to these limitations, the integration of AI and big data offers new possibilities for enhancing disease detection, monitoring, and response strategies on both local and global scales. This review explores the potential of AI-enabled tools and big data systems to support early outbreak detection, real-time surveillance, and predictive modeling. These technologies facilitate the synthesis of diverse datasets, including clinical, genomic, geospatial, and environmental information, enabling a more holistic understanding of disease patterns. Additionally, AI contributes to improved diagnostic accuracy and optimized resource allocation, which are critical during public health emergencies. However, the adoption of these technologies has not been without challenges. Concerns about data privacy, equity in access, algorithmic bias, and over-reliance on automated systems present significant ethical and operational hurdles. In low-resource settings, limited digital infrastructure further complicates implementation. The review also highlights real-world applications from recent outbreaks, such as COVID-19, influenza, and Zika, to demonstrate both the promise and the limitations of AI-driven surveillance. To move forward responsibly, public health systems must adopt a balanced approach that integrates AI capabilities with human oversight. Strategic investment, cross-sector collaboration, and the development of clear ethical frameworks are essential to unlocking the full potential of big data and AI in infectious disease surveillance.

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