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Equity-Aware Multi-Objective vaccine allocation using Machine Learning-Based Risk-Profile stratification
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DOI：10.1016/j.eswa.2026.133816.png)
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Abstract
En 中文
The growing impact of pandemics and infectious disease outbreaks has highlighted the need for vaccine allocation strategies that balance risk-profile protection, equity, and operational feasibility under limited healthcare resources. However, many existing approaches rely on predefined population groups and do not sufficiently integrate data-driven risk-profile information into constrained allocation planning. To address this issue, this study proposes an equity-aware decision-support framework that combines machine learning-based risk-profile stratification with multi-objective vaccine allocation. Publicly available French COVID-19 hospital-surveillance data are reorganized into analytical records to construct operational risk-profile classes for allocation-scenario analysis. A Light Gradient Boosting Machine model classifies these records into ordered risk-profile groups, which are then incorporated into a constrained allocation model. The model aims to maximize protection of higher-priority risk profiles, promote equity across predicted risk-profile groups, and minimize vaccination delays under supply and capacity constraints. The resulting optimization problem is solved using a binary Particle Swarm Optimization algorithm with constraint-handling mechanisms. Computational experiments assess algorithmic performance under a common objective-evaluation budget and examine the repair strategy, policyweight configurations, Pareto-based compromises, classification uncertainty, scalability, and resource-capacity sensitivity. Overall, the framework supports the exploration of risk-profile-based vaccine allocation policies under constrained pandemic-response settings.
Keywords:
Vaccine allocation
Pandemic preparedness
Machine learning
Risk-profile stratification
Multi-objective optimization
Equity-aware decision support
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