Description
The COVID-19 pandemic highlighted the critical role of mathematical and statistical modeling in understanding and mitigating the spread of infectious diseases. We have seen that complex interactions and individualistic decisions of people have required incorporating tools from control and game theory, optimization, data science, and machine learning.
This proposed IRC aims to bring together researchers from mathematics, statistics, epidemiology, and control theory to develop innovative frameworks for modeling infectious disease dynamics and furthermore, designing effective mitigation strategies.
Key research questions include:
- How can we integrate mechanistic epidemic models with data-driven statistical and machine learning approaches?
- What role can control theory and reinforcement learning play in developing adaptive intervention strategies?
- How do heterogeneity, network structure, and behavioral feedback influence epidemic and endemic outcomes?
- How can we explore interactions between related but separately evolving areas (such as evolutionary game theory and control theory) with a specific focus on epidemic modeling and mitigation applications?
- The cluster will explore both theoretical and applied aspects, including stochastic modeling, optimal control, uncertainty quantification, and real-time decision-making under uncertainty.
Event Dates
May 4-7, 2026
Confirmed Participants
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