Advancing Infectious Disease Surveillance through Digital Epidemiology and Artificial Intelligence-Based Outbreak Prediction

Abstract

Digital epidemiology has emerged as an important approach for strengthening infectious disease surveillance by integrating large and diverse sources of real time health information. Digital platforms can provide signals of changing disease activity before conventional surveillance systems detect an outbreak. Artificial intelligence (AI) and machine learning can further analyze these complex datasets and support early outbreak prediction. This review examines the application of digital epidemiology and AI in infectious disease surveillance. Recent studies demonstrate that machine learning, deep learning and natural language processing can improve prediction accuracy. The review also discusses the use of digital approaches during emerging infectious disease outbreaks and their potential to complement conventional surveillance systems. Despite these advances, data quality, algorithmic bias, and limited external validation remain important challenges. Future surveillance systems should therefore combine AI-driven digital signals with established epidemiological methods and expert assessment. Such integration could support earlier detection, improve outbreak preparedness and enable more timely public health responses to emerging infectious disease threats.