Optimizing Cold-Chain Logistics: A Geospatial Model for Medical Supply Delivery in Remote Alaskan Communities

This study addresses the critical challenge of maintaining cold-chain integrity for essential medical supplies, including vaccines and temperature-sensitive pharmaceuticals, during disaster relief operations across the vast and decentralized regions of Arctic Alaska. Current reliance on ad-hoc, reactive delivery methods frequently results in significant spoilage and dangerous delays. We developed a Geospatial Information Systems (GIS) modeling approach that integrates dynamic data layers such as seasonal ice road viability, predicted air-drop zone stability, and recorded extreme ambient temperature fluctuations across 45 remote native villages. The resulting Predictive Logistics Model (PLM) not only optimizes delivery routing but also provides guidance on ideal resource staging and temporary storage points. Validation demonstrates a calculated reduction in spoilage rates by 18% and a decrease in average delivery times by 32 hours compared to historical response data. This framework offers public health agencies a scalable and evidence-based tool for significantly enhancing the security and reliability of humanitarian aid distribution in vulnerable, high-latitude environments.