Artificial intelligence in early warning systems for infectious disease surveillance: a systematic review

SYSTEMATIC REVIEW articleFront. Public Health, 23 June 2025 Sec. Digital Public HealthVolume 13 - 2025 | https://doi.org/10.3389/fpubh.2025.1609615Artificial intelligence in early warning systems for infectious disease surveillance: a systematic reviewIVIsmael Villanueva-Miranda 1GXGuanghua Xiao 1,2YXYang Xie 1,2*1. Department of Health Data Science and Biostatistics, University of Texas Southwestern Medical Center, Dallas, TX, United States2. Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, United States Article metrics View detailsAbstractIntroduction: Infectious diseases pose a significant global health threat, exacerbated by factors like globalization and climate change. Artificial intelligence (AI) offers promising tools to enhance crucial early warning systems (EWS) for disease surveillance. This systematic review evaluates the current landscape of AI applications in EWS, identifying key techniques, data sources, benefits, and challenges.Methods: Following PRISMA guidelines, a systematic search of Semantic Scholar (2018-onward) was conducted. After screening 600 records and removing duplicates and non-relevant articles, the search yielded 67 relevant studies for review.Results: Key findings reveal the prevalent use of machine learning (ML), deep learning (DL), and natural language processing (NLP), which often integrate diverse data sources (e.g., epidemiological, web, climate, wastewater). The major benefits identified inclu...