COVID-19 Exploratory Data Analysis (EDA) Project

Project Overview

This project performs Exploratory Data Analysis (EDA) on global and country-level COVID-19 data. Through various visualizations and statistical techniques, this project aims to:

• Analyze COVID-19 trends globally and locally (by country).
• Understand key features of the data such as infection rates, mortality rates, and vaccination progress.
• Identify patterns, outliers, and correlations in the dataset.
• Create actionable insights using various plots and reports.

Project Goal

The goal of this project is to provide clear insights into the spread and impact of COVID-19 worldwide. Key questions addressed include:

• What are the most affected regions by COVID-19?
• How have case numbers and deaths evolved over time?
• What are the trends in COVID-19 vaccination?

Technologies Used

• Python: For data processing and analysis.
  • Libraries: pandas, numpy, matplotlib, seaborn, plotly
• Jupyter Notebook: For running and documenting the analysis.
• Dash/Plotly: For creating interactive dashboards.
• Git: For version control and collaboration.
• GitHub: For repository hosting and sharing.

Dataset

This project utilizes multiple datasets from various reliable sources to perform an exploratory data analysis of COVID-19 trends globally and at the country level. The following datasets are used in this project:

Dataset Sources:

1. Johns Hopkins University COVID-19 Dataset

   • Source: GitHub Repository - Johns Hopkins University COVID-19
   • Description: This dataset contains daily reports of confirmed COVID-19 cases, deaths, and recoveries at a global level and by country.
   • Data Included:
     • Confirmed cases
     • Deaths and recoveries
     • Geographical information (e.g., country, province)

2. Our World in Data COVID-19 Dataset

   • Source: GitHub Repository - Our World in Data COVID-19
   • Description: This dataset provides detailed COVID-19 data on confirmed cases, deaths, and vaccination information by country.
   • Data Included:
     • Daily reported cases and deaths
     • Vaccination data by country
     • Demographic data and health indicators

3. COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University

   • Source: GitHub Repository - CSSE COVID-19 Data
   • Description: Another source from Johns Hopkins University that provides global case data, including confirmed cases, deaths, and recoveries.
   • Data Included:
     • Global case counts
     • Country-level statistics for the spread of COVID-19

Key Features of the Dataset:

• Confirmed Cases: The number of COVID-19 confirmed cases reported globally and at the country level.
• Deaths and Recoveries: Information on the number of deaths and recoveries due to COVID-19.
• Vaccination Data: Global and country-wise vaccination statistics to understand the progress of vaccination campaigns.
• Time Period: The data spans from the beginning of the COVID-19 pandemic to the present, with daily updates.

Folder Structure

The project structure is organized as follows:

COVID19_EDA_Project/
├── data/
│   ├── raw/               
│   └── processed/        
├── notebooks/
│   ├── data_cleaning.ipynb   
│   ├── exploratory_analysis.ipynb  
│   └── visualizations.ipynb  
├── dashboard/
    ├── app.py
    ├──Procfile
    └── runtime.txt          
├── reports/
│   └── visuals/            
├── .gitignore               
├── LICENSE                  
├── README.md                
└── requirements.txt    

Folder Details:

• data/raw/: Contains the raw datasets that are directly sourced and need processing.
• data/processed/: Contains cleaned and pre-processed data, ready for analysis.
• notebooks/: Jupyter notebooks for carrying out the data analysis, cleaning, and visualizations.
• dashboard/: Contains Dash application files (app.py,procfile).
• reports/visuals/: This folder stores the saved visualizations (graphs, plots, charts) for reports.
• .gitignore: Ensures that unnecessary or temporary files are not tracked by Git (such as compiled Python files, logs, or system files).
• LICENSE: A file that outlines the licensing terms for your project.
• README.md: The file you are currently reading, which contains project information and guidelines.
• requirements.txt: Lists the Python libraries that are required to run the project (you can generate this by running pip freeze > requirements.txt).

Process Involved in this Project

Data Cleaning

The cleaning process involved the following steps for each dataset:

1. Handling Missing Values: Replaced missing values with 0 in numerical columns.
2. Standardizing Column Names: Renamed columns for consistency (e.g., "Country/Region" → "Country").
3. Ensuring Consistent Date Formats: Ensured all date columns follow the YYYY-MM-DD format.
4. Removing Duplicates: Checked and removed duplicate rows, if any.
5. Dropping Irrelevant Columns: Removed unnecessary columns that do not contribute to the analysis.
6. Saving Cleaned Data: All cleaned datasets were saved in the data/processed/ folder for further analysis.

Cleaned Datasets:

• cleaned_confirmed_cases.csv
• cleaned_deaths.csv
• cleaned_recovered.csv
• cleaned_vaccinations.csv

EDA and Visualizations

The following insights were drawn using data visualization techniques.

Global Analysis

• Confirmed Cases Over Time: Observed exponential growth globally with peaks during major pandemic waves.
• Mortality Rates: Fluctuated by region, with some areas significantly higher.
• Vaccination Trends: Developed countries showed rapid increases compared to developing nations.

Country-Level Analysis

• Top 10 Affected Countries: USA, India, and Brazil consistently led in confirmed cases and deaths.
• Case Fatality Rate (CFR): Regions like Italy and the UK showed a higher CFR during initial waves.

Vaccination Insights

• Top Vaccinated Countries: Countries like Israel, UAE, and the USA showed high vaccination rates early.
• Effectiveness: Countries with higher vaccination rates showed a decline in mortality rates.

---

Key Findings

• Global Trends: COVID-19 cases and deaths followed a cyclical pattern, correlating with waves of infection.
• Regional Variations: Developed countries had better healthcare responses but also experienced higher initial cases due to robust testing.
• Vaccination Impact: Strong evidence supports that vaccination campaigns reduced mortality rates in most regions.
• Case Fatality Rate: Globally, the CFR ranged between 1%–3%, with significant variance across regions.

Dashboard

An interactive dashboard has been created to visualize the COVID-19 data and provide real-time insights. The dashboard is built using Dash/Plotly and includes the following features:

• Global Overview: Displays global statistics including total confirmed cases, deaths, and recoveries.
• Country Comparison: Allows users to compare COVID-19 statistics between different countries.
• Trend Analysis: Shows trends over time for cases, deaths, and vaccinations.
• Vaccination Progress: Visualizes the progress of vaccination campaigns globally and by country.
• Interactive Maps: Provides geographical representations of COVID-19 data, highlighting affected regions.

How to Run the Dashboard

1. Ensure you have all the required libraries installed. You can install them using the requirements.txt file:

pip install -r requirements.txt

2. Navigate to the dashboard/ directory:

cd dashboard

3. Run the Dash application:

python app.py

Open your web browser and go to https://covid19edaproject-production.up.railway.app/ to view the dashboard.

Dashboard Files

• app.py: The main file that runs the Dash application.
• callbacks.py: Contains callback functions for interactivity in the dashboard.
• layout.py: Defines the layout and structure of the dashboard.
• assets/: Contains CSS and other assets for styling the dashboard.

The dashboard provides an intuitive and interactive way to explore the COVID-19 data, making it easier to derive insights and understand trends.

Contributing

We welcome contributions to this project! If you'd like to contribute, please follow these guidelines:

1. Fork the repository.
2. Create a new branch for your feature (git checkout -b feature-branch).
3. Make your changes and commit them (git commit -m 'Add feature').
4. Push your changes to your forked repository (git push origin feature-branch).
5. Open a pull request to the main branch of the original repository.

Please ensure that your contributions follow the project’s coding style, and add tests/documentation if applicable.

License

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

Acknowledgments

• Special thanks to the data sources used for this project:

  • Johns Hopkins University COVID-19 Dataset
  • Our World in Data COVID-19 Dataset
  • COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University

• Thanks to the open-source community for the Python libraries used in this project:

  • pandas, numpy, matplotlib, seaborn, plotly, dash

• All contributors and collaborators who made this project possible.