Traditional disease surveillance often relies on manual reporting and small-scale studies, which can be time-consuming and provide limited insights. Big data allows for real-time disease surveillance by integrating data from multiple sources. This can lead to quicker detection of outbreaks and more effective responses. For instance, analyzing search engine queries and social media posts can help identify the early signs of an outbreak before it is officially reported.