Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

mariyeee/Analyzing-and-Forecasting-Healthcare-Access-in-Ethiopia-Using-World-Bank-Data

Domaine:

healthcare

Type de record:

project
Créateur:
mar
Hôte:
Analyzing and Forecasting Healthcare Access in Ethiopia Using World Bank Data # Analyzing-and-Forecasting-Healthcare-Access-in-Ethiopia-Using-World-Bank-Data Healthcare Access Analysis and Forecasting in Ethiopia Overview This project analyzes and forecasts healthcare access in Ethiopia using World Bank data, focusing on key indicators such as health expenditure per capita, physician density, and hospital bed availability. Through exploratory data analysis (EDA), comparative analysis with peer countries (Kenya, Uganda, Rwanda), and time series forecasting with ARIMA models, the project uncovers trends, identifies disparities, and predicts future healthcare metrics. An interactive Plotly Dash dashboard visualizes the findings, and actionable recommendations align with Ethiopia’s Health Sector Transformation Plan (HSTP II) and Sustainable Development Goal 3 (Good Health and Well-being). Objectives Analyze Trends: Identify trends in Ethiopia’s healthcare access metrics (2000–2023). Comparative Analysis: Compare Ethiopia’s metrics with Kenya, Uganda, and Rwanda. Forecasting: Predict healthcare access metrics for 2024–2028 using ARIMA models. Visualization: Develop an interactive Plotly Dash dashboard to showcase trends, comparisons, and forecasts. Policy Recommendations: Provide data-driven insights to support universal health coverage in Ethiopia. Data Sources World Development Indicators (WDI): Health expenditure per capita, life expectancy, population metrics. Health Nutrition and Population Statistics (HNP): Physician density, hospital beds per 1,000 people. Access: World Bank Open Data (WDI, HNP). Methodology Data Cleaning: Handled missing values with linear interpolation. Removed outliers using the IQR method. Normalized data for cross-country comparisons. Exploratory Data Analysis (EDA): Univariate analysis: Descriptive statistics and time series plots. Bivariate analysis: Correlation between expenditure and outcomes. Multivariate analysis: Principal Component Analysis (PCA). Comparative Analysis: Compared Ethiopia with Keny …

Visit

github.com