yohayt/Optimizing-Health-Coverage-in-Ethiopia

Code for the paper titled: Optimizing Health Coverage in Ethiopia: A Learning-augmented Approach and Persistent Proportionality Under an Online Budget Datasets used: 1. Worldpop population estimates: data.humdata.org We used the files for years 2026-2030 and resolution of 1km ^2. 2. Friction data: We used the Malaria atlas friction dataset. Data was downloaded from: data.malariaatlas.org 3. Shapefiles: Downloaded from The Humanitarian Data Exchange website. data.humdata.org 3. Distributional proportion constraints: Our code provides a random numbers for that (the real data is not yet available). 4. Existing facilities: Data is currently not publicly available. Usage: 1. Downloading data: python code_base/preprocess/download.py Rscript code_base/preprocess/download_r.R 2. Setting python environment and install packages (In ubuntu based systems): sudo apt-get install texlive-latex-base texlive-latex-extra texlive-fonts-recommended cm-super dvipng conda env create -f code_base/preprocess/environment.yml conda activate ethiopia after creating the environment 3. preprocessing data and computing distances: python code_base/preprocess/process_data.py Sidama python code_base/preprocess/process_data.py Afar python code_base/preprocess/process_data.py Somali 4.Running experiments: python code_base/EXP1.py python code_base/EXP2.py python code_base/EXP3.py