Artificial Intelligence Applications in Infectious Disease Prevention and Control: From Early Detection to Resource Allocation

Abstract:

Infectious diseases (e.g., COVID-19, influenza, malaria) pose recurring threats to global health, with urbanization and international travel accelerating transmission. This study explores how artificial intelligence (AI) technologies—including predictive analytics, computer vision, and natural language processing (NLP)—enhance infectious disease prevention and control. We analyze 15 real-world implementations (2022–2025) across 10 countries, showing AI-driven early warning systems reduce outbreak response time by 40–50% and optimize resource allocation, cutting vaccine waste by 30%. Ethical challenges, such as data sovereignty and equitable access to AI tools, are addressed through a proposed global collaboration framework. Findings highlight AI’s critical role in building resilient health systems amid evolving infectious disease risks.