The global COVID-19 pandemic underscored the critical need for innovative, scalable, and adaptive solutions to address the complexities of epidemic prevention, mitigation, and response. As infectious disease threats continue to evolve in an interconnected world, artificial intelligence (AI) has emerged as a transformative tool to revolutionize epidemic control. By integrating vast datasets, advanced computational models, and real-time analytics, AI enables unprecedented capabilities in early detection, predictive modeling, and decision-making under uncertainty. This session, “AI for Epidemic Control,” seeks to bridge cutting-edge AI methodologies with public health challenges, fostering interdisciplinary collaboration to advance proactive and data-driven strategies for pandemic preparedness and resilience.

We welcome research that demonstrates the application of AI and operations research techniques to address critical gaps in epidemic management. Topics of interest include, but are not limited to:

• AI-driven Early Surveillance and Alert Systems for Emerging Infectious Diseases;
• AI-aided Predictive Modeling of Epidemic Transmission Risk;
• AI-enhanced Simulations for the Spatiotemporal Spread of Infectous Diseases;
• AI-powered Optimization for Epidemic Control
• Human Behavior Modeling for Epidemic Response using AI Tools.

This session encourages submissions that emphasize methodological rigor, real-world applicability, and ethical considerations in deploying AI systems for public health. We invite contributions spanning theoretical advances, case studies, and cross-domain collaborations (e.g., epidemiology, urban planning, or social sciences). By convening researchers and practitioners, we aim to catalyze actionable innovations that empower policymakers, healthcare systems, and communities to confront future epidemics with agility and precision.