Predictive Modeling of Infectious Disease Outbreaks Through AI and Big Data Analytics

The rapid spread of infectious diseases poses a significant threat to global public health, demanding proactive and precise outbreak management strategies. Recent advancements in artificial intelligence (AI) and big data analytics have provided powerful tools to predict disease patterns, enabling timely interventions and resource optimization. This study explores predictive modeling approaches that integrate heterogeneous data sources, including epidemiological reports, mobility patterns, climate data, and social media trends, to forecast infectious disease outbreaks. Machine learning algorithms, such as ensemble models, recurrent neural networks, and graph-based techniques, are evaluated for their predictive accuracy and adaptability across different disease types. The findings highlight that AI-driven models can identify emerging hotspots, estimate infection trajectories, and support decision-making for public health authorities. Moreover, challenges related to data quality, interpretability, and ethical considerations are discussed to ensure responsible deployment. This research underscores the potential of combining AI with big data analytics to enhance disease surveillance and strengthen global preparedness against infectious threats.