Integration of Big Data Analytics in Public Health Surveillance for Early Detection and Prevention of Epidemic Outbreaks

The rising incidence of epidemics and the acceleration of the dispersal of virulent pathogens signal the inadequacy of public health surveillance systems, reporting constraints, data fragmentation, and the lack of an adequate prediction system. This paper discusses an initial attempt to introduce Big Data Analytics into existing public health systems to characterize early warning systems for epidemic prevention. The proposed framework uses heterogeneous health data and predictive analytics, combined with research and government health data streams, various social media streams, and various health data streams. The system analyzed 1.2 million healthcare-related records collected from various surveillance datasets, used distributed computing frameworks such as Hadoop and Apache Spark, Big Data analytics frameworks, and Machine Learning algorithms, including Random Forest, Decision Tree, and SVM. Data preprocessing was performed to ensure the data analyzed was as accurate as possible, including procedures such as data normalization, noise removal, and missing value imputation. The Random Forest model achieved the highest prediction accuracy of 94.6%, with a precision of 92.8%, a recall of 93.5%, and an F1 score of 93.1%. The proposed framework also showed a 38% decrease in outbreak detection compared to traditional surveillance frameworks. This research shows that Big Data Analytics enhances epidemic prediction, improves the efficiency of outbreak surveillance, and reduces the time taken to make public health decisions. The combination of advanced analytics and healthcare technologies enhances preparedness for future epidemics and reduces disease transmission. The study concludes that data-driven surveillance systems are scalable and reliable tools for contemporary public health management. The data privacy, interoperability, and ethical frameworks of healthcare analytics are highlighted.

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