Resource Allocation Under Behavioral Uncertainty: A Compartmental Stochastic Optimization Framework for Pandemic Preparedness

We develop a compartmental multi-stage stochastic optimization framework to support allocation of critical healthcare resources during infectious disease outbreaks under behavioral and epidemiological uncertainty. The model integrates epidemiological dynamics with adaptive decision-making to capture evolving vaccine hesitancy (VH), disease transmission, and regional healthcare capacity constraints. Using U.S. COVID-19 vaccination data, we evaluate how deployment timing, stockpile size, and equity constraints affect mortality. Results show that early deployment and scalable surge capacity significantly reduce expected deaths, while the cost of enforcing equitable allocation varies over time and peaks during outbreak surges in densely populated regions. Beyond COVID-19, the framework provides a decision-support tool for future high-transmission threats, including emerging “Disease X” scenarios where vaccines may be developed rapidly but uptake remains uncertain. By explicitly modeling the interaction between behavioral uncertainty and resource demand, the approach supports proactive and equitable pandemic preparedness.

Dr. Sandra D. Eksioglu is a Professor and the Hefley Professor in Logistics and Entrepreneurship in the Department of Industrial Engineering at the University of Arkansas. Her research focuses on operations research, with applications in supply chains, sustainable energy systems, and healthcare systems engineering. Dr. Eksioglu’s research is driven by real-world challenges and a dedication to developing solutions that enhance resilience, sustainability, and equitable access to critical resources. Dr. Eksioglu has co-authored numerous peer-reviewed publications and a textbook adopted in more than 20 countries worldwide. She is a Fellow of IISE and an active member of INFORMS and ASEE.