Predictive Modeling for Vaccine Distribution and Uptake Optimization

Abstract

Efficient vaccine distribution and high population uptake are essential for controlling infectious disease outbreaks and minimizing societal disruption. While advances in biomedical science have accelerated vaccine development, logistical con straints, demand uncertainty, and behavioral factors continue to challenge large scale immunization efforts. Predictive modeling offers a systematic approach for anticipating distribution needs, optimizing allocation strategies, and improving vaccine uptake across diverse populations. This study investigates predictive modeling techniques that integrate epidemiological data, supply chain dynamics, and behavioral indicators to support data driven vaccine distribution and uptake optimization. The proposed approach demonstrates how machine learning and analytical forecasting can enhance decision making for public health planning and resource coordination.