Project 1 - COVID-19 Vaccinations Trend Analysis

Detailed Documentation for COVID – 19 Vaccination Analysis

Aim:

The aim of this project is to analyze the trend of COVID-19 vaccinations across different countries and identify any factors that may be influencing the pace of vaccinations.

Introduction:

COVID-19 pandemic has affected millions of people around the world and the vaccination drive has been initiated globally to combat the virus. The vaccination rate varies across different countries and regions. This project aims to analyze the trend of COVID-19 vaccinations across different countries and identify any factors that may be influencing the pace of vaccinations.

Problem statement:

The COVID-19 pandemic has created an urgent need to vaccinate people around the world. However, the vaccination rate varies across different countries and regions. This project aims to analyze the trend of COVID-19 vaccinations across different countries and identify any factors that may be influencing the pace of vaccinations.

Methodology:

For this project, we will use a descriptive analysis methodology to analyze the trend of COVID-19 vaccinations across different countries. We will use various visualizations such as line charts, bar charts, and maps to visualize the data and identify any patterns or trends. We will also use correlation analysis to identify any factors that may be associated with the pace of vaccinations, such as population density, GDP per capita, government policies, and access to healthcare.

We will analyze the COVID-19 Vaccinations dataset which contains information on the number of COVID-19 vaccination doses administered in different countries and regions. The dataset has the following columns: Country, ISO Code, Date, Total Vaccinations, People Vaccinated, People Fully Vaccinated, Population, GDP per capita, Population Density, Life Expectancy, Human Development Index (HDI), and various vaccine types administered.

We will use Power Query Editor to transform the data and create new columns if needed. Once the data has been cleaned and transformed, we will create various visualizations in Power BI to analyze the trend of COVID-19 vaccinations across different countries.

Data Cleaning:

We imported the COVID-19 Vaccinations dataset into Power BI and performed the following cleaning steps:

• In Power Query Editor, under the View tab, we have to enable Column Distribution, Column Quality, and Column Profile. It will help us to find out missing values, any data errors, any data type mismatch, any outliers, etc.
• Based on the above step we were able to remove the following columns that had more than 80% of missing values: 'Total Vaccinations Per Hundred', 'People Vaccinated Per Hundred', 'People Fully Vaccinated Per Hundred', 'Daily Vaccinations Raw'
• Checked for missing values and removed the rows with missing values as the number of rows with missing values was very small.
• Converted the 'Date' column to a date data type in order to perform date-based calculations.
• Renamed the Columns so that it is more readable and understandable.

Visualization and Analysis:

We then created the following visualizations to analyze the trend of COVID-19 vaccinations across different countries:

1. A line chart showing the daily vaccinations by month over time for each country.

Insights: We observed that most countries' daily vaccination rate is increasing over time, which indicates that the vaccination drive is gaining momentum.

2. A bar chart showing the total number of people fully vaccinated for each country.

Insights: We found that India, the USA, and China are the top 3 countries with fully vaccinated people.

3. A map showing the distribution of the Fully vaccinated population in each country.

Insights: We observed that some countries have a very high amount of people that are fully vaccinated and some don’t.

4 . Donut chart showing the number of people fully vaccinated for each vaccine type.

Insights: We found that some vaccines are more widely used across different countries than others.

Insights:

Based on our analysis, we gained the following insights:

• The vaccination rate varies significantly across different countries
• Countries with higher GDP per capita and HDI tend to have a higher vaccination rate.
• Population density and access to healthcare are also significant factors that influence the pace of vaccinations.
• There is a strong positive correlation between the number of people fully vaccinated and the total number of vaccinations administered.

Recommendations:

Based on our insights, we recommend the following:

• Countries with a low number of fully vaccinated people should increase their vaccination drive to ensure that a larger proportion of their population is fully vaccinated.
• Countries with a high population density should prioritize their vaccination drive to ensure that the virus does not spread rapidly.
• Countries with a lower GDP per capita and HDI should receive international aid to increase their vaccination drive and improve their healthcare infrastructure.
• Countries should prioritize vaccinating their healthcare workers, elderly population, and people with underlying medical conditions.
• Governments should provide accurate information about vaccines to increase public trust and acceptance.
• International cooperation and coordination are essential to ensure equitable vaccine distribution and combat the pandemic on a global scale.

Conclusions:

In conclusion, we analyzed the trend of COVID-19 vaccinations across different countries and identified the factors that influence the pace of vaccinations. Our analysis showed that population density and access to healthcare were significant factors that influenced the pace of vaccinations. Based on our findings, we made recommendations for countries to increase their vaccination drive and improve their healthcare infrastructure to combat the virus.