Big Data Analytics frameworks have become essential tools for pandemic prediction and healthcare resource allocation management in modern public health systems. These frameworks integrate distributed computing, machine learning, artificial intelligence, and real-time data processing techniques to analyze large-scale and heterogeneous datasets generated from hospitals, laboratories, mobility systems, social media platforms, and public health surveillance networks. By processing structured and unstructured data, big data systems can identify early outbreak signals, track disease transmission patterns, and predict future infection trends with high accuracy. In addition, these frameworks support healthcare resource allocation by optimizing the distribution of medical supplies, hospital beds, ventilators, vaccines, and healthcare personnel during pandemics. Technologies such as Hadoop, Apache Spark, cloud computing platforms, and real-time streaming systems enable scalable and efficient data processing. Predictive models such as time-series forecasting, regression analysis, and deep learning-based epidemic models enhance decision-making capabilities for healthcare authorities. Despite their benefits, challenges such as data privacy, data heterogeneity, model uncertainty, infrastructure limitations, and real-time processing constraints remain significant. This study explores big data analytics frameworks, literature contributions, methodologies, advantages, and limitations in pandemic prediction and healthcare resource management systems.
