Design concept for a scenario based COVID-19 simulation tool of the German Aerospace Center.

For my graduation project I had the opportunity to work with scientists of the German Aerospace Center on a software concept for a new cutting-edge model, able to simulate the spread of the COVID-19 pandemic in Germany.

Problem space

Many existing simulation tools are either only suitable for educational purposes or too complex for a regular person to use. The German Aerospace Center and the Helmholtz-Centre for Infection Research set out to create a new type of model that would be accessible to the public through an easy to use interface.

The model

The model from Martin J. Kühn et al. is able to provide geographically and temporally high-resolution simulations of the spread of COVID-19. At its core the SIR-type model is based on differential equations that calculate the transitions between states (compartiments) of an infection with SARS-CoV-2.

Durations of phases of COVID-19 in the model.

User Research

During the research phase of the project we conducted qualitative interviews with the target groups of the project: interested laymen, politicians of the German parliament and experts in the field of virology and modelling.

Prof. Ralf Dringenberg

Director of the School of Applied Design Schwäbisch Gmünd

Dr. Janosch Dahmen MdB

Member of the Bundestag (Bündnis 90/Die Grünen)

Dr. Martin Joachim Kühn

Scientific researcher at the German Aerospace Center

Layout and Wireframes

To suits the needs of a broad range of users the interface is structured in different views among which users can switch.

The content of this information dense interface neatly sits on a 16-column grid.

Scenario-based user interface

The interface enables users to easily compare different scenarios with one another and through that get an understanding how to interpret the dynamic of the pandemic.

Clicking on a scenario card turns the card around and deactivates the scenario in the graph.

The list with monospaced numbers makes it easy to compare the metrics of different scenarios.

The time horizon defines the length of the simulation. Percentage increases are calculated from the current day up to the time horizon.

Browse scenarios

Users can browse through a collection of pre-created scenarios and also create their own scenarios in the overview.

Scenarios are grouped by most seen, forward facing and backward facing simulations.

Scenarios can be added to the scenarios panel by drag’n drop or a simple click.

User can create their own scenarios and set the key parameter ‘hidden infections’ upon creation.

Adjusting the model through contact rates

In the ‘contact rate’ view users can adjust the model by contact rates and their durations. They can set custom names and by that define NPIs (non-pharmaceutical interventions) that influence the projection of the model.

Users can adjust contact rates and their duration for each of the four locations of the model. They can give these sections a name to describe a containment measure.

The NPI history is a list that provides a quick overview of past  NPIs (non-pharmaceutical interventions) the government took to contain the spread of SARS-CoV-2.

Accessible model parameters

The third section of the UI is specifically dedicated to experts that want access to all parameters of the model. With that they can create their own scenarios, fine-tune them and compare them to test their own hypotheses.

End notes

It was a great experience to work with the scientists of the German Aerospace Center on this concept. We look back with fond memories on the time collaborating with our partners and thank them for their time, effort and trust.

This concept was created on basis of the publication of Martin J. Kühn et al. (2020): Assessment of non-pharmaceutical interventions mitigating the spread of Covid-19 in Germany using demographic information and spatial resolution