COVID-19
Impact on Anxiety and Depression

Description

This project was developed for the Visual Analytics course at RIT Croatia.
The main goal was to let the data speak — crafting visualizations that require no explanation. Visual immediacy and clarity were fundamental design principles throughout the entire process.

The analysis focused on the global mental health impact of COVID-19, particularly depression and anxiety.
Open-source datasets were integrated and analyzed to uncover trends across age groups and income levels using interactive dashboards.

Key findings include:

  • Young people in high-income countries were the most psychologically affected by the pandemic.

  • Anxiety and depression spiked after 2020, especially among individuals under 30.

  • Depression rates decrease with rising income, but start climbing again beyond a certain wealth threshold — forming a U-shaped curve visible in GDP scatterplots.

  • Visual evidence also showed that air pollution and technology overuse may correlate with higher mental distress in certain regions.

Overall, the project shows how well-designed data stories can surface complex socio-economic patterns and offer actionable insights.


Tools

I began with Excel for basic data exploration and merging multiple sources. The next step was using Python, where I relied on Pandas for data cleaning and Plotly and Seaborn for generating exploratory and comparative visualizations.

  • Plotly allowed for interactive graphs to explore trends dynamically.

  • Seaborn was useful for quick correlation plots and category-based comparisons.

  • Power BI was used to build the main dashboard, offering an accessible and user-friendly way to filter and explore the data.

  • I also experimented with D3.js for more advanced and fully customizable web-based visualizations.

As always, the core of the project was a solid and coherent data structure — everything was built upon clean, well-merged datasets to ensure meaningful and accurate insights.