Umang-Shikarvar/NTDs-in-Africa

# Neglected Tropical Diseases in Africa: A Data-Driven Analysis An analysis of Lymphatic Filariasis (LF) and Soil-Transmitted Helminthiasis (STH) eradication programs across 40+ African countries, using WHO surveillance data and World Bank socioeconomic indicators spanning 2000–2023. **Team:** Umang Shikarvar · Romit Mohane · Soham Gaonkar · Shreyans Jain --- ## What This Project Does Seven hypotheses about NTD program performance are tested through regression analysis, ANOVA, correlation analysis, and K-means clustering. Key questions include: Has coverage improved over time? Does GDP predict coverage? What actually drives national treatment rates? Are programs responsive to disease burden? --- ## Project Structure | File | Description | |------|-------------| | `wa.ipynb` | Core analytical notebook — data pipeline, statistical tests, all visualisations | | `index.html` | Interactive web dashboard | | `style.css` | Dashboard styling | | `main.js` | Chart loading and page interactions | | `assets/charts.json` | Extracted Plotly chart configurations (server use) | | `assets/charts.js` | Same data as inline JS (works with `file://` protocol) | | `extract_charts.py` | Script to regenerate chart assets from the notebook | | `data/raw/` | Original WHO Excel source files | | `data/processed/` | Cleaned and merged datasets | --- ## Running Locally Open `index.html` directly in a browser. Charts load from `assets/charts.js` without needing a server. Alternatively, serve with a local HTTP server: ```bash python3 -m http.server ``` Then open `localhost`. --- ## Regenerating Charts After modifying `wa.ipynb`, execute the notebook and regenerate chart assets: ```bash jupyter nbconvert --to notebook --execute wa.ipynb --output wa.ipynb python3 extract_charts.py ``` --- ## Data Sources - **WHO Global NTD Database** — LF and STH programme coverage data - **World Bank Open Data** — GDP per capita, health expenditure, sanitation access, urbanisation, …