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With the continuous rise of the COVID-19 cases worldwide, it is imperative toensure that all those vulnerable countries lacking vaccine resources canreceive sufficient support to contain the risks. COVAX is such an initiativeoperated by the WHO to supply vaccines to the most needed countries. Onecritical problem faced by the COVAX is how to distribute the limited amount ofvaccines to these countries in the most efficient and equitable manner. Thispaper aims to address this challenge by first proposing a data-driven riskassessment and prediction model and then developing a decision-making frameworkto support the strategic vaccine distribution. The machine learning-based riskprediction model characterizes how the risk is influenced by the underlyingessential factors, e.g., the vaccination level among the population in eachCOVAX country. This predictive model is then leveraged to design the optimalvaccine distribution strategy that simultaneously minimizes the resulting riskswhile maximizing the vaccination coverage in these countries targeted by COVAX.Finally, we corroborate the proposed framework using case studies withreal-world data.

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