Coronavirus disease 2019 (COVID-19) is a severe, novel virus that has spread globally. The implementation of a combination of public health interventions is required to reduce viral spread and avoid overwhelming acute care systems. Once available, an effective vaccination will further mitigate the impact of the COVID-19 pandemic. However, decision makers will initially need to prioritise access to limited vaccine stockpiles as these will be insufficient to vaccine the whole population. The aim of this study is to identify optimal vaccine allocation strategies defined by age and risk target groups, coverage, effectiveness, and cost of vaccine, within a dynamic context where other public health responses and population behaviour change. In this study we use an epidemiological model of COVID-19 that has been enhanced to produce expected costs and Quality Adjusted Life Year results as well as total cases, hospitalisations, deaths, and net monetary benefit. We use the model to simulate hypothetical scenarios where vaccine is allocated beginning on October 15, 2020 with vaccine assumptions ranging from moderately optimistic to worst-case scenario. Net monetary benefit is used as the objective for optimisation. In a scenario with a sterilizing vaccine that is 80% effective, a stockpile sufficient for 40% population coverage, and prioritisation of those over the age of 60 at high risk of poor outcomes, active cases are reduced by 29.2% and net monetary benefit increased by $297 million dollars, relative to an identical scenario with no vaccine. The relative impact of prioritisation strategies varies greatly depending on concurrent public health interventions, for example, polices such as school closures and senior contact reductions have similar impacts on incremental net monetary benefit when there is no prioritisation given to any age or risk group (147 vs. 120 million, respectively), but when older and high risk groups are given priority, the benefit of school closures is much larger than reducing contacts for seniors (iNB 122 vs. 79 million, respectively). Results demonstrated that rank ordering of different prioritisation options varied greatly by prioritisation criteria, with different vaccine effectiveness and coverage, and by concurrently implemented policies. The results of this paper have three key policy implications: (i) that optimal vaccine allocation will depend on the public health policies, and human behaviours in place at the time of allocation; (ii) the outcomes of vaccine allocation policies can be greatly supported with interventions targeting contact reduction in critical sub-populations; and (iii) the identification of the optimal strategy depends on which outcomes are prioritised.
A Net Benefit Approach for the Optimal Allocation of a COVID-19 Vaccine