Overview
This exploratory data analysis examined COVID-19 infection patterns across Romania using a dataset snapshot from October 8, 2020. The analysis began September 27 and concluded October 10, 2020, as part of the Freecodecamp "Data Analysis with Python: from zero to pandas" course.
Key Findings
Infection Status
The project analyzed total infections, daily case evolution, testing patterns, and age-group distributions across Romanian counties. The data revealed concerning trends at the time of analysis.
Testing and Positive Rate
The percentage of positive tests appeared to be rising (as of 08.10.2020), although the number of tests had stayed relatively the same. This suggested either increasing community spread or more targeted testing strategies.
Age Group Analysis
The 10–19 age demographic showed rising case numbers since July but had not experienced substantial percentage increases by early October. However, given viral incubation periods, future trends remained uncertain at the time.
County-Level Patterns
A clear correlation emerged: "The greater the population, the greater the chance of infection." This pattern was visible in scatterplot analysis comparing county populations with infection rates. Bucharest stood out as a significant outlier requiring additional investigation.
Visualizations
The project included interactive Plotly visualizations showing daily case evolution, test volume and positive rate trends, age-group distributions, county population versus infection percentage, and animated evolution maps showing infection spread over time.
The interactive charts from the original analysis are embedded below from the Jovian notebook:
Resources
The entire project is available on Jovian, containing full Python and Pandas code with interactive Plotly graphs.