COVID-19 Global Data Tracker

A comprehensive data analysis project that visualizes and explores global COVID-19 trends using the Our World in Data (OWID) dataset.

Project Overview

This project analyzes COVID-19 pandemic data across different countries, examining cases, deaths, and vaccination rates over time. Through various data visualization techniques, the notebook reveals patterns, comparisons between countries, and insights into the global impact of the pandemic.

Objectives

• Import and clean COVID-19 global data from reliable sources
• Analyze time trends of cases, deaths, and vaccinations
• Compare metrics across countries and regions
• Visualize trends with various chart types and interactive maps
• Identify correlations between different pandemic metrics and country characteristics
• Provide interactive tools for custom analysis of specific countries and time periods

Tools and Libraries Used

• Python 3.6+: Core programming language
• Pandas: Data manipulation and analysis
• NumPy: Numerical operations
• Matplotlib & Seaborn: Static data visualization
• Plotly Express: Interactive maps and charts
• Jupyter Notebook: Interactive computing environment
• ipywidgets: Interactive UI components for custom analysis

Dataset

The analysis uses the Our World in Data (OWID) COVID-19 dataset, which includes:

• Daily and cumulative case/death counts
• Testing data
• Vaccination statistics
• Population demographics
• Healthcare capacity metrics
• Geographic information

How to Run the Project

1. Clone this repository

   git clone https://github.com/alphac137/covid19-global-data-tracker.git
   cd covid19-global-data-tracker
   

2. Install required packages

   pip install -r requirements.txt
   

3. Download the dataset

   • The owid-covid-data.csv file should be in the repository
   • For the most recent data, download from Our World in Data

4. Run the Jupyter Notebook

   jupyter notebook COVID19_Global_Data_Tracker.ipynb
   

5. Explore the interactive analysis

   • Use the widgets in the "Interactive Analysis" section to select countries and date ranges
   • Customize visualizations to focus on specific metrics

Key Insights

The analysis revealed several important insights:

1. Global Impact: The pandemic affected countries differently, with significant variations in case and death rates.

2. Death Rates: Substantial differences in mortality rates were observed, which can be attributed to factors like healthcare capacity, demographics, and reporting methods.

3. Vaccination Progress: Notable disparity in vaccination coverage, with high-income countries generally achieving higher rates.

4. Socioeconomic Factors: Strong correlations between COVID-19 metrics and factors like GDP per capita, human development index, and healthcare capacity.

5. Wave Patterns: Multiple infection waves occurred across regions at different times, suggesting the influence of policy differences and variant emergence.

Limitations and Future Work

• Data reporting varies significantly between countries
• Testing strategies differ, affecting reported case numbers
• Future analyses could incorporate policy data, mobility information, and variant tracking

Acknowledgments

• Data provided by Our World in Data
• Project developed as part of a data analysis learning journey

License

This project is licensed under the MIT License - see the LICENSE file for details.