  Early on during a pandemic, vaccine availability is limited, requiring
prioritisation of different population groups. Evaluating vaccine allocation is
therefore a crucial element of pandemics response. In the present work, we
develop a model to retrospectively evaluate age-dependent counterfactual
vaccine allocation strategies against the COVID-19 pandemic. To estimate the
effect of allocation on the expected severe-case incidence, we employ a
simulation-assisted causal modelling approach which combines a compartmental
infection-dynamics simulation, a coarse-grained, data-driven causal model and
literature estimates for immunity waning. We compare Israel's implemented
vaccine allocation strategy in 2021 to counterfactual strategies such as no
prioritisation, prioritisation of younger age groups or a strict risk-ranked
approach; we find that Israel's implemented strategy was indeed highly
effective. We also study the marginal impact of increasing vaccine uptake for a
given age group and find that increasing vaccinations in the elderly is most
effective at preventing severe cases, whereas additional vaccinations for
middle-aged groups reduce infections most effectively. Due to its modular
structure, our model can easily be adapted to study future pandemics. We
demonstrate this flexibility by investigating vaccine allocation strategies for
a pandemic with characteristics of the Spanish Flu. Our approach thus helps
evaluate vaccination strategies under the complex interplay of core epidemic
factors, including age-dependent risk profiles, immunity waning, vaccine
availability and spreading rates.
