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Early on during a pandemic, vaccine availability is limited, requiringprioritisation of different population groups. Evaluating vaccine allocation istherefore a crucial element of pandemics response. In the present work, wedevelop a model to retrospectively evaluate age-dependent counterfactualvaccine allocation strategies against the COVID-19 pandemic. To estimate theeffect of allocation on the expected severe-case incidence, we employ asimulation-assisted causal modelling approach which combines a compartmentalinfection-dynamics simulation, a coarse-grained, data-driven causal model andliterature estimates for immunity waning. We compare Israel's implementedvaccine allocation strategy in 2021 to counterfactual strategies such as noprioritisation, prioritisation of younger age groups or a strict risk-rankedapproach; we find that Israel's implemented strategy was indeed highlyeffective. We also study the marginal impact of increasing vaccine uptake for agiven age group and find that increasing vaccinations in the elderly is mosteffective at preventing severe cases, whereas additional vaccinations formiddle-aged groups reduce infections most effectively. Due to its modularstructure, our model can easily be adapted to study future pandemics. Wedemonstrate this flexibility by investigating vaccine allocation strategies fora pandemic with characteristics of the Spanish Flu. Our approach thus helpsevaluate vaccination strategies under the complex interplay of core epidemicfactors, including age-dependent risk profiles, immunity waning, vaccineavailability and spreading rates.

Evaluating vaccine allocation strategies using simulation-assisted causal modelling