JAN-AUG 2022
Epidemic X

Epistemix came to my team wanting a way for their users to analyze results from the simulations run on their platform. We aspired to create an interface tailored for their target users, public health policymakers, that empowers a data driven decision making process.

Data visualization

Anushree Abhyankar (Researcher)
Ryan Adibi (Researcher)
Anita Sun (Designer)
Mabel Tsado (Designer)

The client
Epistemix is a Pittsburgh based startup that provides a platform for running simulations with their proprietary coding language (FRED). Their clients include the World Health Organization and the University of Pittsburgh. Our main point of contact was their CSO.

My role
as the design lead

I worked in a team of 3 designers and 2 researchers. I was the design lead, in charge of:

  • design strategy: leading meetings to map out product systems
  • design system: taking the prototype from low to high fidelity
  • client relations: preparing presentations and activities to facilitate feedback

Individually, I designed two features (smart suggestions and commenting) and animated the prototype interactions.

Takeaway: adapting to time constraints

I worked through an interesting challenge with time constraints. Our client changed our project halfway through (the original ask was for a low code tool), so our original 7 month timeline shrunk to 2.5 months to research and design from scratch. I emerged from this challenge an adaptable teammate and leader. To optimize our time, I requested we prioritize conceptual usefulness of the product over usability of the UI in both our design and research.


In the current state, policy makers go to Epistemix to have simulations run. The simulation results are sent as spreadsheet files and Google Drive folders with graphs, making the data analysis process long and difficult for the policy makers.Epistemix wanted to improve the data analysis process for their users. As such, we asked ourselves: how might we empower public health policy makers to make policy recommendations based on data provided from Epistemix’s simulations?

Epidemic X is a browser based tool for public health policy makers using simulations from Epistemix to compare different possible outcomes of a pandemic. It enables efficient exploration, comparison, and evaluation of simulation results to aid in the policy recommendation process.

In the demo video, I walk through our target user's (Isabella) journey using Epidemic X.

4 rounds of user testing, 8 prototype iterations

We implemented an iterative design process with user testing every 2 weeks. I facilitated weekly feedback sessions with the CSO for design and content feedback. I often reached out to engineers and data scientists within Epistemix to hear feedback on the feasibility of our designs.

Research into design
I bridged the transition from research to design by leading the creation of our design principles. These principles helped us navigate what features to keep or cut.

Promoting meaningful exploration
Policy makers have to go through thousands of graphs when analyzing simulation results. I wanted to make sure that they wouldn’t be wasting time. I designed smart suggestions to help with this: Epidemic X suggests content, such as historical case studies, based on the user’s behavior.

Enhancing collaboration
Policies are not made alone. Team discussions are vital to the policy making process, but there currently aren’t many asynchronous methods to collaborate. With the rise of stay-at-home work from the pandemic, asynchronous workflows are crucial for teamwork. This is why I designed comments to enhance digital collaboration.

Wrapping up
Success metrics

For future testing, we put together success metrics that can be used:

  • Likert scale survey: we can measure how well Epidemic X promoted meaningful exploration and balanced breadth and depth of results with a rating system of strongly disagree to strongly agree.
  • Comparing number of meetings: the number of meetings after using Epidemic X should go down compared to before using it if asynchronous collaboration was successful.
  • Conversion rate: we recommend offering Epidemic X as a beta product first and tracking conversation rate of beta testers to full time users.

From this project, I emerged a better design leader able to handle complications from time constraints. I loved rising to the challenge and figuring out new ways to implement the design process under unexpected time pressures. Working in a cross functional team also gave me great experience communicating design ideas to facilitate feedback from stakeholders within Epistemix.

RAI: Robot Anomaly Interface
Serena Wang © 2023