  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.
