Adam Quek 2022-09-10

To create 3 informative visualizations about Covid-19 using Python in a Jupyter notebook, starting with the data set at https://www.kaggle.comdatasetssambelkacemcovid19-algeria-and-world-datasetcode.

Variables from downloaded data has been changed to code friendly format. The data are further split into 6 normalised tables and hosted within a SQLite3 database (using "RSQLite" packages).

The normalised tables include

• "location" (Primary key: "Entity", contains the geographical information for the 104 countries)
• "demographics" (Primary key: "Entity", contains demographic information for the 104 countries)
• "economy" (Primary key: "Entity", contains another feature on GDP Per Capita)
• "hospital" (Primary key: "Entity", contains features on Hospital Bed per 1000 People and Medical Doctors per 1000 People)
• "temperature" (Primary key: "Entity", contains another feature on Average Temperature per year)
• "covid" (Foreign key: "Entity", contains Number of Daily tests, cumulative number of COVID-19 Cases and Deaths)

All visualisation are carried out in the single JuPyteR notebook in the repository. The visualisation consists of:

• Choropleth on Total Number of COVID-19 -- Graphical representation of distribution of COVID-19 cases across the 104 countries.

• Relationship of COVID-19 Deaths with Percentage of Population Aged 65 and Over -- Graphical representation shows that the proportion of COVID-19 death (per capita) is correlated with the proportion of elderly population (per capita). -- An Ordinary Least Squares (OLS) regression trendline is added to aid identifying the relationship.

• Relationship of COVID-19 Cases with GDP Per Capita -- Graphical representation shows that the proportion of COVID-19 cases (per capita) is correlated with the GDP of the nation. -- An Ordinary Least Squares (OLS) regression trendline is added to aid identifying the relationship.