  How to strategically allocate the available vaccines is a crucial issue for
pandemic control. In this work, we propose a mathematical framework for optimal
stabilizing vaccine allocation, where our goal is to send the infections to
zero as soon as possible with a fixed number of vaccine doses. This framework
allows us to efficiently compute the optimal vaccine allocation policy for
general epidemic spread models including SIS/SIR/SEIR and a new model of
COVID-19 transmissions. By fitting the real data in New York State to our
framework, we found that the optimal stabilizing vaccine allocation policy
suggests offering vaccines priority to locations where there are more
susceptible people and where the residents spend longer time outside the home.
Besides, we found that offering vaccines priority to young adults (20-29) and
middle-age adults (20-44) can minimize the cumulative infected cases and the
death cases. Moreover, we compared our method with five age-stratified
strategies in \cite{bubar2021model} based on their epidemics model. We also
found it's better to offer vaccine priorities to young people to curb the
disease and minimize the deaths when the basic reproduction number $R_0$ is
moderately above one, which describes the most world during COVID-19. Such
phenomenon has been ignored in \cite{bubar2021model}.
