konstantinburkin / vaccination_rates

Data Analysis project of world vaccination rates for MSU course "Data analysis with Python".

Vaccination rates

Introduction

Evaluation of successful vaccination programs is important to combat potential pandemics in the future. Here, the countries were determined where herd immunity against COVID-19 was achieved, hence making these vaccination programs efficient. For this the end-date was predicted when each country can finish making vaccinations by modeling vaccination dinamics with logistic regression.

Data

Dataframe from Kaggle was used that described COVID-19 World Vaccination Progress. Data was collected across every country and in the continents in general. The population ratio for herd immunity against common viruses including SARS-Cov-2 was found online.

The result of this project is a dataframe containing id the names of the countries and the predicted date when the population is fully vaccinated.

Files

• Vaccination_rates.ipynb - Main notebook with description of the project, code, graphs, and commentaries.
• Vaccination_rates_supplementary.ipynb - Additional code needed to convert the notebook to html.