Motivated by COVID-19 vaccine allocation, where vulnerable subpopulations are
simultaneously more impacted in terms of health and more disadvantaged in terms
of access to the vaccine, we formalize and study the problem of resource
allocation when there are inherent access differences that correlate with
advantage and disadvantage. We identify reducing resource disparity as a key
goal in this context and show its role as a proxy to more nuanced downstream
impacts. We develop a concrete access model that helps quantify how a given
allocation translates to resource flow for the advantaged vs. the
disadvantaged, based on the access gap between them. We then provide a
methodology for access-aware allocation. Intuitively, the resulting allocation
leverages more vaccines in locations with higher vulnerable populations to
mitigate the access gap and reduce overall disparity. Surprisingly, knowledge
of the access gap is often not needed to perform access-aware allocation. To
support this formalism, we provide empirical evidence for our access model and
show that access-aware allocation can significantly reduce resource disparity
and thus improve downstream outcomes. We demonstrate this at various scales,
including at county, state, national, and global levels.

针对 COVID-19 疫苗分配中存在的易受影响却难以获得疫苗的弱势群体，我们形式化并研究了资源分配问题，标识出减少资源差距作为关键目标，并展示其作为更复杂后续影响的替代指标的作用。我们提出了一种具体的访问模型，有助于量化给定分配对有利群体和不利群体之间的资源流动，基于他们之间的访问差距。然后，我们提供了一种访问感知的分配方法。直观地说，得到的分配利用更多疫苗在易受影响人口更多的地区以减少访问差距和整体不公。出乎意料的是，进行访问感知的分配通常不需要了解访问差距。为了支持这个形式化表示，我们提供了我们的访问模型的经验证据，并展示了访问感知的分配可以显著减少资源差距，从而改善后续结果。我们在县级、州级、国家级和全球级别展示了这一点。