Integrating AI, Machine Learning, and Big Data Analytics for Public Health Surveillance

Abstract

The quick development of digital technologies has changed public health surveillance systems, making it easier to find, track, and respond to diseases. This paper examines the amalgamation of Artificial Intelligence (AI), Machine Learning (ML), and Big Data Analytics as a holistic framework for the augmentation of public health surveillance infrastructure. Conventional surveillance techniques encounter substantial constraints, such as delayed reporting, inadequate data collection, and restricted predictive capability. AI-driven systems that work together can process massive amounts of structured and unstructured data in real time by using many different data sources, such as electronic health records, social media streams, environmental sensors, and mobile health apps. This review analyzes the contemporary applications of predictive modeling, natural language processing, and deep learning algorithms in outbreak detection, disease forecasting, and syndromic surveillance. We look at case studies that show how early warning systems for infectious disease outbreaks and better use of resources during public health emergencies have gotten better. There are important problems that need to be solved, such as worries about data privacy, algorithmic bias, problems with interoperability, and the need for strong validation frameworks. The results show that successful integration needs people from different fields to work together, standardized data protocols, and ethical governance structures. This coming together of technologies gives us new chances to make global health security stronger and build public health systems that can handle new health threats.

This paper also suggests a scalable implementation roadmap for health authorities that want to use these technologies with their current infrastructure. We look at cost-effectiveness metrics and workforce training needs that are necessary for long-term deployment. The combination of cloud computing platforms and edge computing solutions is looked at to make real-time data processing possible. We also talk about the international cooperation frameworks that are needed for cross-border surveillance harmonization and data sharing agreements. Our analysis concludes that future public health preparedness fundamentally depends on strategic technological investments and policy innovations supporting evidence-based decision-making.

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