AI for Precision Vaccine Distribution

Abstract

Vaccine distribution systems frequently rely on periodic demand estimates and fixed replenishment rules that respond poorly to local epidemiological changes, population mobility, cold-chain disruptions, and unequal healthcare access. This study identifies a research gap in the absence of an integrated artificial intelligence framework that simultaneously predicts demand, estimates cold-chain failure risk, and protects geographic and socioeconomic equity during vaccine allocation. A precision distribution framework is proposed in which spatiotemporal forecasting, graphbased logistics modelling, and equity-constrained optimization are combined within a continuously updated decision architecture. The system is designed to generate facility-level vaccine requirements while accounting for disease incidence, historical uptake, demographic vulnerability, travel accessibility, stock availability, storage capacity, and temperaturemonitoring signals. Unlike conventional cost-centred distribution models, the proposed approach treats preventable stock-outs, vaccine wastage, excessive travel burden, and inequitable coverage as interconnected operational outcomes. Uncertainty-aware predictions are incorporated so that allocation decisions remain cautious in districts with incomplete, delayed, or unreliable health data. The framework also supports interpretable recommendations by reporting the demand, risk, accessibility, and fairness factors influencing each shipment decision. The proposed research establishes a foundation for adaptive, transparent, and equitable vaccine logistics in resource-constrained and geographically diverse public-health systems