APPLICATIONS OF BIG DATA ANALYTICS IN EPIDEMIOLOGY: OUTBREAK DETECTION AND PANDEMIC PREDICTION

Abstract

Big data analytics has revolutionized epidemiology by providing powerful tools for outbreak detection and pandemic prediction. Traditional epidemiological surveillance systems are limited by time delays, incomplete reporting, and geographic constraints. In contrast, big data approaches incorporate diverse data sources, including electronic health records, mobile applications, genomic sequencing, and social media, enabling real-time monitoring of disease trends. This article analyzes the applications of big data analytics in early outbreak detection and the prediction of pandemic dynamics. A mixed-method review of recent studies and case examples was conducted, focusing on the role of machine learning, artificial intelligence (AI), and predictive modeling in epidemiology. The results indicate that big data analytics enhances early warning capacity, improves accuracy in forecasting epidemic curves, and assists policymakers in resource allocation. However, challenges such as data privacy, standardization, and ethical concerns must be addressed. The study concludes that integrating big data analytics into global health systems is vital for future preparedness against pandemics.