Global Tuberculosis Data Analysis with Python

An exploratory data analysis (EDA) project analyzing global tuberculosis (TB) surveillance data from the World Health Organization (WHO) between 2000 and 2022.

---

Workflow Overview

The analysis covers data cleaning, calculating epidemiological metrics, analyzing regional differences, and creating visualizations in Python.

flowchart TD
    A[Raw WHO TB Data<br/>.csv] --> B[Data Cleaning & Prep<br/>Pandas]
    B --> C[Calculate Key Metrics<br/>Incidence & Mortality Rates]
    C --> D[Regional Analysis<br/>6 WHO Regions]
    D --> E[Trend Analysis<br/>Yearly Changes 2000-2022]
    E --> F[Visualizations<br/>Matplotlib & Seaborn]

Loading

---

Questions Explored

1. Regional Distribution: Which geographic regions account for the highest proportion of global TB cases?
2. Long-Term Trends: How have incidence and mortality rates changed over the past two decades?
3. Mortality vs. Detection: How does the mortality-to-incidence ratio differ between low-income and high-income regions?
4. Recent Impact: How did the 2020–2021 period affect case notifications and reporting?

---

Project Structure

Python_for_data_sci/
├── data/
│   ├── raw/                 # Downloaded WHO CSV data
│   └── processed/           # Cleaned data files
├── notebooks/
│   ├── 01_data_cleaning.ipynb
│   └── 02_exploratory_analysis.ipynb
├── src/
│   └── helpers.py           # Helper functions for plots and calculations
├── requirements.txt         # Python libraries
├── LICENSE
└── README.md

---

Setup & How to Run

1. Set Up Environment

# Clone the repository
git clone https://github.com/Abdul3442/Python_for_data_sci.git
cd Python_for_data_sci

# Create a virtual environment
python3 -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

2. Launch Jupyter

jupyter lab

Open the notebooks in the notebooks/ folder to run through the data cleaning and visualization steps.

---

Key Observations

• Regional Concentration: Southeast Asia and Africa continue to carry the majority of the global burden, accounting for more than 65% of reported cases.
• Progress Over Time: Global incidence rates showed a gradual decline between 2000 and 2019, though progress varies significantly by country.
• Reporting Disruptions: Case notifications noticeably dipped in 2020, reflecting healthcare disruptions during the pandemic.

---

Data Source

The dataset used in this project comes from the World Health Organization (WHO) Global Tuberculosis Programme:

• WHO TB Data Portal

---

License

This project is licensed under the MIT License.