Yu-Xing Tian

EGAD: an Empirical Bayes-Driven Gittins allocation framework for imported infectious disease surveillance under resource constraints

Rapid global mobility expansion complicates imported infectious disease surveillance, impeding detection of asymptomatic carriers in high-volume passenger flows. Existing standard operating procedure (SOP)-based, briefing-driven, and baseline reinforcement learning (RL) models are frequently confronted with risk...