Integrating Artificial Intelligence and Data Analytics for Advancing Disease Surveillance and Early Public Health Decision-Making

Artificial intelligence (AI) and data analytics are reshaping disease surveillance by enabling earlier detection, faster analysis, and more informed public health decision-making. This review examines how AI-driven models, predictive analytics, and real-time data systems strengthen global surveillance infrastructures. By integrating diverse data sources—including clinical records, genomic sequences, environmental signals, mobility data, and social media—AI enhances situational awareness and identifies outbreak patterns before traditional reporting systems. Applications such as machine learning–based forecasting, natural language processing for syndromic surveillance, and spatial-temporal modeling support timely interventions and resource planning. Case studies from COVID-19, malaria, and dengue monitoring illustrate the transformative impact of these technologies. However, challenges persist, including data fragmentation, algorithmic bias, privacy risks, and gaps in digital infrastructure—particularly in low-resource settings. Effective deployment requires strong governance, ethical frameworks, and capacity building to ensure transparency, interoperability, and equitable benefits. Looking ahead, opportunities lie in precision public health, One Health integration, federated learning, and international collaboration that respects data sovereignty. When implemented responsibly, AI and analytics can shift disease surveillance from reactive response to anticipatory action, strengthening global preparedness and advancing equitable public health protection. .