  Since early 2020, the world has been dealing with a raging pandemic outbreak:
COVID-19. A year later, vaccines have become accessible, but in limited
quantities, so that governments needed to devise a strategy to decide which
part of the population to prioritize when assigning the available doses, and
how to manage the interval between doses for multi-dose vaccines. In this
paper, we present an optimization framework to address the dynamic double-dose
vaccine allocation problem whereby the available vaccine doses must be
administered to different age-groups to minimize specific societal objectives.
In particular, we first identify an age-dependent
Susceptible-Exposed-Infected-Recovered (SEIR) epidemic model including an
extension capturing partially and fully vaccinated people, whereby we account
for age-dependent immunity and infectiousness levels together with disease
severity. Second, we leverage our model to frame the dynamic age-dependent
vaccine allocation problem for different societal objectives, such as the
minimization of infections or fatalities, and solve it with nonlinear
programming techniques. Finally, we carry out a numerical case study with
real-world data from The Netherlands. Our results show how different societal
objectives can significantly alter the optimal vaccine allocation strategy. For
instance, we find that minimizing the overall number of infections results in
delaying second doses, whilst to minimize fatalities it is important to fully
vaccinate the elderly first.
