INFLUENZA


























Interactive Data Visualization


2. semester individual project


Overview
INFLUENZA is an interactive data visualization based on global influenza data from 2009 to 2024.
 


The project explores how seasonal illness patterns can be translated into a visual system that allows comparison across countries, climates and time.



The visualization focuses on six countries: Germany, Iceland, Iran, Japan, North Korea and the United Arab Emirates. Each of them represents a different geographical and climatic context, from temperate European patterns to desert climate conditions and East Asian seasonal rhythms.

The project was developed in the course Programmiertes Entwerfen 2. The task was to work with a large dataset and transform it into an interactive visual system.

Instead of explaining the data mainly through text, the interface had to make relationships visible through form, rhythm, colour and interaction.This made the dataset more than a collection of case numbers. It became a way to observe how influenza behaves differently across regions.



 





Reading seasonal patterns
Influenza is strongly connected to time. Peaks, baselines and recurring seasonal changes are often difficult to understand in a linear table. For this reason, the visualization uses a radial timeline structure.

Each cycle represents the rhythm of a year, allowing users to see recurring winter peaks, quieter summer periods and irregular spikes across multiple years.

The radial layout makes the dataset readable as a pattern. The 2009 H1N1 pandemic spike becomes visible across countries, while seasonal differences between northern, southern and warmer regions can be compared through the same visual structure.




The colour system was derived from national colour identities. Instead of using generic chart colours, each country received a visual reference connected to its flag or cultural colour system. 






Data and scopeThe dataset was based on influenza-related sources including WHO FluNet, the Global Influenza Surveillance and Response System and Our World in Data. From the larger dataset, I focused on selected countries and key indicators that could give a clearer view of influenza activity and severity.  

The final visualization works with cleaned data rows for the six selected countries and maps several FluNet categories, including reported influenza-like illnesses, acute respiratory infections, severe respiratory cases, deaths and normalized values per thousand outpatients.





Visualisation of chosen countries on ourworldindata.org




Because the source data was large and structurally complex, a major part of the project was not only designing the interface, but preparing the data itself.

Date formats had to be standardized, incomplete structures reduced, and categories renamed so that the visualization could stay understandable without becoming overloaded.




InteractionUsers can explore the data by switching between metrics, comparing countries and hovering over individual data points. The interface makes it possible to move between overview and detail without leaving the visual system.


Additional settings allow adjustments to dot size, opacity, background brightness, grid visibility, month labels, tooltip behaviour and contrast. These controls were useful for testing how much visual support the system 
needs before the data becomes readable.

The interaction is intentionally simple. The main focus is not on adding many features, but on allowing users to observe patterns through direct exploration.




Interactive live demonstration here:
https://neu-blush.vercel.app/  https://neu-blush.vercel.app/   https://neu-blush.vercel.app/  https://neu-blush.vercel.app/


Technical implementationThe prototype was built as a web-based visualization using JavaScript, gmynd.js and jQuery. Git was used for version control, and the final prototype was deployed through Vercel.

The cleaned dataset contains 49,890 rows for the selected countries, with a larger original dataset of 96,875 rows. 

The implementation included custom functions for data preparation, aggregation, rendering and interaction.

The final codebase connects data processing with dynamic visual output. Performance optimization, caching and efficient DOM handling were important because the interface needed to remain responsive while working with a large dataset.






JONAS WIENBERG
PORTFOLIO
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