<![CDATA[The rapid spread of infectious diseases remains a major global health threat, and early detection is vital to minimize their impact. This research investigates the role of predictive modeling using big data in the early detection of infectious disease outbreaks. The primary objective of this study is to assess the effectiveness of big data systems in forecasting potential outbreaks and the implications of these forecasts for public health systems. The study employs machine learning-based predictive models to process large health datasets, including electronic health records, sensor data, and social media information. The results demonstrate that the predictive model achieved an accuracy rate of 87%, significantly surpassing traditional methods in terms of early detection. By integrating various data sources such as medical records, sensor networks, and real-time digital traces, the system is capable of providing more accurate, timely predictions, which can greatly improve the ability of public health authorities to respond effectively to emerging health threats. Furthermore, the application of big data in public health not only improves the speed of response but also enhances the allocation of resources, allowing for more targeted and efficient interventions. Despite these successes, challenges remain, particularly in relation to data quality, privacy, and regulatory issues, which could hinder the broader implementation of such systems. Thus, collaboration between government agencies, healthcare institutions, and technology developers is essential to overcome these obstacles and ensure the sustainable integration of big data into public health infrastructures. This research highlights the significant potential of big data to transform public health responses, offering valuable insights for future epidemic management strategies.]]>
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