COVID-19 Regional Analysis: South Asia vs Middle East

📖 Overview

This project provides an in-depth analysis of the COVID-19 pandemic's impact across South Asia and the Middle East. It explores trends, vaccination progress, statistical relationships, and predictive insights, offering visualizations and actionable insights.

Key Focus Areas:

• COVID-19 Trends: Analyze total cases, positivity rates, and ICU patient trends.
• Vaccination Progress: Investigate the relationship between GDP, population density, and vaccination rates.
• Statistical Testing: Perform hypothesis testing (t-test) to compare vaccination progress across regions.
• Predictive Modeling: Use linear regression to predict total cases based on key factors.

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✨ Key Features

• 📈 Time-Series Trends: Visualize total cases, vaccination progress, and positivity rates with interactive plots.
• 🔗 Automated Updates: Weekly updates of COVID-19 data using GitHub Actions.
• 📊 Correlation Analysis: Uncover relationships between GDP, vaccination progress, and population density.
• 🔍 Predictive Modeling: Linear regression to identify factors influencing total cases.
• 🔬 Hypothesis Testing: Compare vaccination progress between South Asia and the Middle East.
• 📂 Interactive Analysis: Use dropdown selectors for country-specific insights.

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📊 Dataset

• Source: Our World in Data
• File: data/owid-covid-data.csv
• Description:
  • Global COVID-19 statistics: total cases, deaths, vaccination data.
  • Economic and demographic data: GDP per capita, population density.

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🛠️ How to Set Up and Run

1️⃣ Clone the Repository

git clone https://github.com/yourusername/COVID-19-Regional-Analysis.git
cd COVID-19-Regional-Analysis

2️⃣ Set Up a Virtual Environment

It’s recommended to use a virtual environment for dependency isolation:

python -m venv venv

Activate the virtual environment:

• Windows:

  venv\Scripts\activate

• macOS/Linux:

  source venv/bin/activate

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Run the Analysis

Option 1: Using Jupyter Notebook

Navigate to the notebook directory and start the Jupyter Notebook:

cd notebook
jupyter notebook

Open covid-analysis.ipynb in your browser.

Option 2: Automated Update Workflow

This project includes a GitHub Actions Workflow to automate weekly updates of the dataset. The dataset updates every Sunday at midnight UTC. Ensure the workflow is enabled in the repository's Actions tab.

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📂 Project Structure

COVID-19-Regional-Analysis/
├── data/                    # Dataset directory
│   ├── owid-covid-data.csv  # COVID-19 data file
│   └── last_updated.txt     # Metadata for last update timestamp
├── notebook/                # Jupyter notebooks for analysis
│   └── covid-analysis.ipynb # Main notebook
├── scripts/                 # Python scripts for automation
│   └── update_data.py       # Automates data updates
├── .github/workflows/       # GitHub Actions workflows
│   └── update_data.yml      # Workflow for weekly data updates
├── images/                  # Example visualizations
├── requirements.txt         # Python dependencies
└── README.md                # Project documentation

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📈 Example Visualizations

Total Cases Trend

Vaccination Progress

GDP Per Capita vs Total Cases

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⚡ Results and Insights

Key Findings:

1. Vaccination Progress:

   • Middle Eastern countries exhibit stronger correlation with GDP than South Asia.
   • Socio-economic constraints contribute to slower vaccination progress in South Asia.

2. Positivity Rates:

   • South Asia experiences spikes in positivity rates due to testing delays.
   • Middle East demonstrates consistent positivity rate trends.

3. Regression Analysis:

   • GDP per capita and vaccination progress are significant predictors of total cases.
   • Population density has a weaker correlation.

4. Hypothesis Testing:

   • Significant differences exist in vaccination progress between the regions (p-value < 0.05).

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🚀 Future Improvements

• Extend analysis to additional regions for global comparisons.
• Integrate advanced predictive models (e.g., Random Forest, XGBoost).
• Include government stringency index and healthcare capacity metrics.
• Develop a fully interactive dashboard with Plotly Dash.

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🤝 Contributions

Contributions are welcome! Feel free to:

1. Fork the repository.
2. Submit pull requests for new features or fixes.
3. Report any issues or suggestions.

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🔗 References

• Our World in Data
• COVID-19 Data Repository

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