# Optimizing Health Coverage in Ethiopia: A Learning-augmented Approach and Persistent Proportionality Under an Online Budget

## TL;DR

This paper introduces HARP, an optimization tool for Ethiopia's health system expansion that maximizes coverage under budget constraints, incorporating learning-augmented and greedy algorithms for improved planning.

## Contribution

The paper presents a novel decision-support framework and algorithms for sequential health facility planning under budget uncertainty, with proven empirical effectiveness in Ethiopia.

## Key findings

- Algorithms outperform expert recommendations in coverage maximization.
- The approach ensures proportionality targets are met at each planning step.
- Empirical tests show significant improvements across multiple regions.

## Abstract

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.