Integrating AI Technologies in Public Health Surveillance: A Multidisciplinary Approach

Abstract

Public health surveillance plays a crucial role in monitoring, preventing, and managing disease outbreaks and health threats. The integration of Artificial Intelligence (AI) into public health surveillance systems has opened new avenues for real-time data analysis, early detection, and efficient resource allocation. This paper explores a multidisciplinary approach that combines data science, epidemiology, public policy, and healthcare to improve surveillance mechanisms using AI. We highlight how machine learning, natural language processing, and predictive analytics are revolutionizing health monitoring, outbreak prediction, and response strategies. The study also examines the ethical, legal, and social implications of deploying AI in public health, emphasizing the need for responsible and equitable implementation. Real-world case studies, such as AI's role in COVID-19 tracking and vector-borne disease prediction, are analyzed to provide insights into best practices and challenges. The paper concludes by outlining future research directions and recommendations for fostering cross-sector collaboration to enhance public health outcomes through AI-driven surveillance systems.