Semia-BEN-AMARA/MASA_Health_Logistics_Optimization

Optimizing medical supply chains in rural Ghana using spatial Data Science. Implementing K-Means and MCLP models to maximize healthcare coverage via drone delivery. # MASA_Health_Logistics_Optimization Optimizing medical supply chains in rural Ghana using spatial Data Science. Implementing K-Means and MCLP models to maximize healthcare coverage via drone delivery. ## Project Overview **MASA** is a Data Science initiative designed to optimize the medical supply chain in rural Ghana. Our analysis identifies critical "blind spots" where hospitals remain isolated from emergency supplies, and proposes a data-driven drone delivery network to bridge the gap. This project was developed during the **Jedha Bootcamp Full Stack Data Science** program. ## Problem Statement - **The Gap**: 665 health facilities are currently out of reach of the existing drone network. - **The Goal**: Design an optimized, scalable, and cost-effective hybrid network to deliver blood, vaccines, and essential medicines. ## Technical Stack - **Data Engineering**: Web scraping (BeautifulSoup) & ETL pipelines. - **Analysis**: Spatial EDA using `Geopandas` and `Folium`. - **Machine Learning**: K-Means Clustering & MCLP (Maximal Covering Location Problem). - **Deployment**: Interactive dashboard built with `Streamlit`. ## Key Results - **Optimized Coverage**: Projected increase of 18% to 25% in population access. - **Cost Efficiency**: Proposed CAPEX of **$1.2M** vs. traditional **$16M** infrastructures. - ## Live Demo You can access the interactive Dispatch System here: **MASA | Medical Air Supply Application** *(Note: Use the "Demo Mode" button on the login page for immediate access.)* ## Team - **Semia Ben Amara** (Data Scientist / Engineer) - Alicia Marzouk, Mathieu Le Faou, Athanor SAVOUILLAN