<![CDATA[The spread of infectious diseases is a global problem that requires effective approaches for prediction and management. In recent years, Big Data technology has become a major concern in the healthcare field due to its ability to quickly collect, store and analyze large and diverse volumes of data. This opens up new opportunities to improve prediction and management of the spread of infectious diseases. This research aims to investigate the role of Big Data technology in predicting and managing the spread of infectious diseases. We want to identify effective methods for using big data to predict disease spread patterns and manage responses to them. The research method used in this research is a qualitative method in the form of literature analysis about the use of Big Data technology in the health sector, case studies of the implementation of Big Data systems to predict the spread of disease. The research results show that Big Data technology can improve predictions of the spread of infectious diseases by integrating data from various sources, including clinical, geographic, demographic and social data. Integrated Big Data systems can provide a better understanding of the factors that influence the spread of disease and enable faster and more effective decision making in responding to outbreaks. The conclusion of this research is that it confirms that Big Data technology has great potential in improving the prediction and management of the spread of infectious diseases. By effectively leveraging big data, we can improve our understanding of the dynamics of disease spread and implement more timely and efficient intervention strategies. Therefore, further investment and development in Big Data technology in the health sector is essential to strengthen capacity to face global health challenges.]]>
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