Outbreak Detective (HAS Event Game)

Preface

This game was built for a Health Analytics Society event under tight time constraints. I used Claude as a development assistant to help me move faster while designing and implementing the experience.

Overview

Outbreak Detective is an interactive Streamlit game designed to help students think through real public health surveillance tradeoffs. Players investigate multi-source outbreak signals, decide where an outbreak likely started, and then choose interventions under a fixed budget to compare outcomes.

Learning Goals

• Practice interpreting surveillance data with different strengths and weaknesses.
• Distinguish early signals (wastewater/RNA) from lagging clinical indicators.
• Make intervention decisions with tradeoffs in cost, timing, and expected impact.

Game Flow

1. Phase 1 (Detection): Teams review three exhibits and identify the likely origin zone.
2. Phase 2 (Forecast): Teams view projected trend behavior if no intervention is applied.
3. Phase 3 (Response): Teams build a response plan with a 10-point budget and evaluate impact.

Tech Stack

• Python
• Streamlit
• NumPy
• Pandas
• Plotly
• SciPy (optional fallback logic exists if unavailable)
• Altair

Project Structure

• app.py: Landing page and game instructions.
• pages/Phase 1 .py: Detection phase with exhibit stepping and facilitator controls.
• pages/Phase 2 & 3.py: Forecast + intervention simulator with score and KPI outputs.
• pages/assets/: Static visual assets used by the game.

Quick Start

1. Create and activate a Python environment.
2. Install dependencies:

pip install -r requirements.txt

3. Run the app:

streamlit run app.py

4. Open the local URL Streamlit prints (typically http://localhost:8501).

Notes

• The app is designed for facilitated group play during events/workshops.
• Phase files currently include spaces/symbols in filenames to match your existing setup.