用优化算法帮埃塞俄比亚高效分配医疗资源，确保各地公平覆盖。
Optimizing Health Coverage in Ethiopia: A Learning-augmented Approach and Persistent Proportionality Under an Online Budget
- 基于学习增强的决策框架，动态规划医疗点建设顺序。
- 在预算不确定下，实现各区域覆盖率最大化且满足比例目标。
- 适合政策制定者和公共卫生管理者用于资源优先级评估。
为响应联合国可持续发展目标3（全民健康覆盖），埃塞俄比亚卫生部正加强基层卫生站以扩大基本医疗服务可及性。然而受限于预算与多重优先事项，每年仅能实施部分建设计划，亟需优化框架指导区域间资源分配。本文提出健康可及性资源规划工具HARP，基于严谨的序贯设施规划优化框架，在预算不确定性下最大化人群覆盖，并在每一步时间点满足区域特有的比例性目标。我们设计了两种算法：(i) 学习增强方法，优于专家单步建议；(ii) 适用于多步规划的贪心算法，均具备强最坏情况近似保证。通过与埃塞俄比亚公共卫生研究所及卫生部合作，在三个地区多种规划场景中验证了该方法的实证有效性。
原文摘要 · Abstract (English)
As part of nationwide efforts aligned with the United Nations' Sustainable Development Goal 3 on Universal Health Coverage, Ethiopia's Ministry of Health is strengthening health posts to expand access to essential healthcare services. However, only a fraction of this health system strengthening effort can be implemented each year due to limited budgets and other competing priorities, thus the need for an optimization framework to guide prioritization across the regions of Ethiopia. In this paper, we develop a tool, Health Access Resource Planner (HARP), based on a principled decision-support optimization framework for sequential facility planning that aims to maximize population coverage under budget uncertainty while satisfying region-specific proportionality targets at every time step. We then propose two algorithms: (i) a learning-augmented approach that improves upon expert recommendations at any single-step; and (ii) a greedy algorithm for multi-step planning, both with strong worst-case approximation estimation. In collaboration with the Ethiopian Public Health Institute and Ministry of Health, we demonstrated the empirical efficacy of our method on three regions across various planning scenarios.
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