Vaccine Allocation

Vaccine allocation research focuses on optimizing vaccine distribution to maximize population-level health outcomes, particularly during pandemics, while addressing inequities in access. Current research employs diverse modeling approaches, including agent-based models simulating individual behavior and disease spread, compartmental ODE models incorporating parameter uncertainty via Bayesian inference and Gaussian processes, and machine learning techniques like graph neural networks and reinforcement learning to optimize allocation strategies across various spatial scales. These studies aim to improve the efficacy of vaccination campaigns by identifying optimal allocation policies that consider factors such as population vulnerability, access disparities, and mobility patterns, ultimately informing public health decision-making and resource management.