This research uses artificial intelligence to analyse immune-system data and predict vaccine effectiveness. By identifying early biological signals associated with strong, long-lasting immunity, the work aims to improve vaccine design, personalise vaccination strategies, and support development of universal vaccines capable of protecting against rapidly evolving infectious diseases.

This research develops human–AI methods to prioritize massive data streams, especially in public health. By combining expert expectations with extreme value theory, it ranks events by contextual importance, reducing alert overload. Deployed nationally, the approach triaged thousands of outbreaks and data-quality issues, making big data interpretable, actionable, and life-saving decisions.