VaxEquity: A Data-Driven Risk Assessment and Optimization Framework for Equitable Vaccine Distribution

TL;DR

This paper presents a data-driven framework that assesses risks and optimizes vaccine distribution to promote equitable access in vulnerable countries, using machine learning and decision-making models.

Contribution

It introduces a novel risk prediction model combined with an optimization framework for equitable vaccine distribution, addressing a critical global health challenge.

Findings

Effective risk prediction for vaccine distribution

Optimized strategies improve coverage and reduce risks

Case studies validate the framework's practical utility

Abstract

With the continuous rise of the COVID-19 cases worldwide, it is imperative to ensure that all those vulnerable countries lacking vaccine resources can receive sufficient support to contain the risks. COVAX is such an initiative operated by the WHO to supply vaccines to the most needed countries. One critical problem faced by the COVAX is how to distribute the limited amount of vaccines to these countries in the most efficient and equitable manner. This paper aims to address this challenge by first proposing a data-driven risk assessment and prediction model and then developing a decision-making framework to support the strategic vaccine distribution. The machine learning-based risk prediction model characterizes how the risk is influenced by the underlying essential factors, e.g., the vaccination level among the population in each COVAX country. This predictive model is then leveraged to design the optimal vaccine distribution strategy that simultaneously minimizes the resulting risks while maximizing the vaccination coverage in these countries targeted by COVAX. Finally, we corroborate the proposed framework using case studies with real-world data.