Exploring the Impact of Data Science on Public Health Interventions

Abstract

The integration of data science into public health interventions is revolutionizing disease prevention, outbreak management, and healthcare resource allocation. By leveraging big data analytics, artificial intelligence (AI), and machine learning (ML), public health agencies can analyze large-scale epidemiological data, predict disease trends, and optimize healthcare strategies. This paper explores the role of data science in public health, covering key applications such as AI-powered disease surveillance, predictive analytics for epidemic forecasting, and data-driven decision-making in healthcare policies. Additionally, challenges such as data privacy, equity in healthcare access, and ethical concerns are discussed, along with emerging trends in AI-driven public health interventions.

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