Global COVID-19 Vaccination Data Analysis and Visualisation

A data-analysis project examining global COVID-19 vaccination progress through dataset cleaning, exploration, and visualisation.

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🎯 Objectives

• Analyse worldwide vaccination trends and compare progress across regions and income groups.
• Clean, validate, and transform raw vaccination data to enable reliable insights.
• Produce visual representations that highlight coverage differences, rollout speed, and vaccine usage patterns.
• Incorporate external socioeconomic information to uncover broader relationships and disparities.

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🗃️ Dataset

File: data/country_vaccinations.csv

The dataset can be downloaded from Kaggle via this link.

The data in the dataset is a comprehensive collection of daily COVID-19 vaccination data by country, gathered from Our World in Data’s GitHub repository. Country-level vaccination data is compiled into a single file. This file is then merged with location data files to include vaccination source information before the author uploads it on Kaggle.

The data contains the following information:

No.	Attribute Name	Description
1.	country	Country for the vaccination data.
2.	iso_code	ISO code for the country.
3.	date	Date of the data entry.
4.	total_vaccinations	Absolute number of total immunisations.
5.	people_vaccinated	Number of people who received at least one dose.
6.	people_fully_vaccinated	Number of people who received all doses prescribed by the vaccination protocol.
7.	daily_vaccinations_raw	Raw number of vaccinations administered for that date. This column will not be used in favour of daily_vaccinations.
8.	daily_vaccinations	Number of vaccinations administered for that date.
9.	total_vaccinations_per_hundred	Ratio (in percent) vaccination number and total population.
10.	people_vaccinated_per_hundred	Ratio (in percent) between vaccinated people and total population.
11.	people_fully_vaccinated_per_hundred	Ratio (in percent) between fully vaccinated people and total population.
12.	daily_vaccinations_per_million	Ratio (in parts per million) between daily vaccination number and total population.
13.	vaccines	Total number of vaccines used in the country.
14.	source_name	The source of the information.
15.	source_website	The website of the information source.

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📡 External Data Sources & APIs

Additional contextual information is incorporated using two public APIs:

• World Bank Country API: Supplies country income-level classifications (Low, Lower Middle, Upper Middle, High). These classifications are merged into the dataset using ISO country codes to support comparative socioeconomic analysis.

• REST Countries API: Provides population figures for each country. A mapping layer is included to correct naming inconsistencies between the dataset and the API.

These external datasets enhance the main vaccination data by enabling population-based grouping and income-level comparisons.

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📁 Project Structure

covid19_vaccination_analysis/
│
├── data/
│   └── country_vaccinations.csv                        # Dataset
│
├── figures/                                            # Folder for exported visualisations
│   ├── Top_10_Countries_by_Vaccination_Rate.png
│   ├── Bottom_10_Countries_by_Vaccination_Rate.png
│   ├── Global_Usage_of_Different_Vaccines.png
│   └── ...
│
├── .gitignore                                          # Files/folders excluded from Git
│
├── LICENSE                                             # MIT License
|
├── README.md                                           # Project overview and instructions
│
├── covid19_vaccination_analysis.ipynb                  # Main notebook
│
└── requirements.txt                                    # Dependency list for reproducibility

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🔧 Setup & Execution

1. Clone the repository

   git clone https://github.com/ChokZB/covid19_vaccination_analysis.git
   

2. Create a virtual environment (optional but recommended)

   python -m venv venv
   source venv/bin/activate        # macOS / Linux
   venv\Scripts\activate           # Windows
   

3. Install dependencies

   Install all required packages using the provided requirements.txt:

   pip install -r requirements.txt
   

4. Run the notebook

   jupyter notebook covid19_vaccination_analysis.ipynb
   

   or open it directly in Colab.

   

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📷 Visual Outputs

Some previews of visualisations produced during the analysis, exported from the notebook for reference.

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📈 Results & Insights

• Income-group comparisons, supported by classifications obtained from the World Bank API, reveal clear disparities in average vaccinations per hundred people, with higher-income economies consistently achieving substantially greater coverage than lower-income groups.

• Population-based analysis, informed by population figures retrieved through the REST Countries API, shows that smaller countries exhibit the widest spread in vaccination rates, whereas medium and large population groups display more stable and clustered distributions.

• Scatter-plot patterns indicate no strong linear association between the number of vaccine types administered and national vaccination coverage.

• Global daily vaccination activity reached its highest point on 27 June 2021, reflecting intensified rollout phases, strategic dose-interval adjustments, and improved supply during mid-2021.

• Vaccine-usage distributions highlight uneven reliance on specific vaccine brands, demonstrating varied national strategies and availability conditions across regions.

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🧑‍💻 Author

Chok Zu Bing

GitHub: @ChokZB

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🪪 Licence

This project is released under the MIT License.