Application of big data in infectious disease surveillance: Contemporary challenges and solutions

The integration of big data into infectious disease surveillance signifies a radical paradigm shift in public health, driven by technological advancements and expanded data gathering capabilities. This convergence highlights the incorporation of large data reservoirs and strengthens and complements traditional disease monitoring strategies. This study examines the difficulties associated with using big data for infectious disease surveillance, considering issues with data privacy and integrity as well as the requirement for strong analytical frameworks. It explores the approaches for overcoming these barriers, highlighting the importance of data depersonalization, secure data transmission methods, and the creation of accurate predictive models. The discussion concludes by showcasing exemplary scenarios in which big data deployments have enhanced disease monitoring and governance, all while contemplating potential directions for the rapidly evolving landscape of this field.