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I designed an admin "Mission Control" tool that transforms complex epidemiological parameters into a guided configuration workflow and provides real-time visibility into system health during live simulations.
Running a live epidemiology simulation is operationally complex: facilitators must configure scientifically valid scenarios, monitor fast-changing system behavior, and intervene at the right moment.
The goal was to design a centralized "Mission Control" that makes complex epidemiological modeling accessible to non-technical educators. This early design phase focused on establishing a scalable architectural foundation for the simulation ecosystem.
Setting up a valid simulation required adjusting many interdependent variables—such as transmission rate and population density—leading to high cognitive load and a risk of setup error.
During live runs, facilitators lacked a clear way to understand system health or participant behavior in real-time, making it difficult to know when to intervene.
These early prototypes established the user flows and information architecture that informed the tool's current implementation by the Fathom design team. By mapping out complex epidemiological logic into a human-readable interface, we moved the project from abstract research into a viable product roadmap.
A unified, glanceable interface that aggregates system health, participant activity, and key metrics. This reduces noise and allows facilitators to quickly assess stability and prioritize action.
Result: Improved situational awareness and faster decision-making during live simulations.
A step-by-step setup flow that converts complex epidemiological parameters into a guided process with built-in validation.
Result: Prevented invalid configurations and significantly reduced setup errors and cognitive load.
A control layer that provides immediate access to high-impact actions such as pausing simulations or issuing alerts.
Result: Reduced response time during critical moments and enabled facilitators to stabilize simulations before failure states occurred.
A visualization layer that compares predicted epidemiological curves with real-time outcomes.
Result: Enabled researchers to validate model behavior and adjust parameters when simulations deviated from expected patterns.
A tiered system for expert users that supports deeper experimentation and "what-if" scenario testing without overwhelming standard workflows.
Result: Balanced usability for educators with flexibility for researchers.
Visualizes how participant decisions—such as lockdowns or resource allocation—affect infection spread and system stability.
Result: Made abstract policy effects visible and strengthened learning outcomes during simulation debriefs.
Tracks biological and operational indicators such as infection rates, testing coverage, and contact tracing performance.
Result: Helped facilitators identify weaknesses in containment strategy and guide intervention timing.
Monitors critical thresholds (e.g., hospital capacity) and surfaces early warning signals of system collapse.
Result: Enabled proactive intervention and prevented simulation failure.
Centralizes critical alerts and maintains a full audit trail of interventions for post-simulation analysis.
Result: Improved operational control and supported structured evaluation after each run.
