Infectious disease outbreaks pose serious threats to global health, economic stability, and societal well-being. An effective response system needs to answer the following key questions in a timely manner: how will a disease spread, what interventions can control them, and how to monitor populations to enable early warnings? However, current approaches often rely on fragmented or delayed data, such as clinical case reports, incomplete testing coverage, and/or the inability to see how infected people move and interact. These factors make it difficult to combine different sources of information and adapt to rapidly changing conditions. These limitations can delay response efforts and reduce their effectiveness. This project aims to develop next-generation epidemic intelligence systems that improve how public health agencies forecast, manage, and monitor infectious diseases. By enabling earlier detection, more targeted interventions, and better situational awareness, the project will strengthen public health infrastructure, support informed decision-making, and enhance resilience to future outbreaks. The project also contributes to education by training students at multiple levels, engaging K-12 learners, and providing open resources to increase participation in data science and public health. To meet these goals, this project develops a unified, data-driven framework that integrates epidemic forecasting, intervention planning, and surveillance under diverse and evolving data