# Disease Spread & Dice Simulation Recreation Prompt
SORBET – Disease Transmission Probability Simulator
Overview
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https://iwant2study.org/lookangejss/biology/ai/rollDiceInfection_Interactive_20250807033820/
https://iwant2study.org/lookangejss/biology/ai/sweelingRolldiceInflectionSORBETv2.zip
Meet an immersive HTML5 educational simulation that explores the probabilistic nature of disease spread with dice-based mechanics—bridging theory and experience. Inspired by the acclaimed Socially Responsible Behaviour through Embodied Thinking (SORBET) Project from NIE, this tool leverages embodied learning to deliver powerful insights into epidemiology, probability distributions, and the Law of Large Numbers.
marking by theory and observed data.
input field with rubric based marking
Why SORBET Is So Powerful
The original SORBET Project—a collaboration led by Dr Kenneth Y. T. Lim at NIE—puts students into a virtual environment that models viral diffusion using augmented reality and open-source tools, helping them internalize safe distancing concepts through embodied experiences rather than abstract charts or lectures.(Corporate NTU) By doing so, learners perceive how their individual actions ripple through space and affect others—transforming cognition into real social understanding.
Demonstrated Impact
In trials with Secondary Two students, participants showed a statistically significant rise in their agreement with the statement: “I can make a difference to how Singapore responds to the COVID‑19 pandemic.”(singteach.nie.edu.sg) This shift in mindset underscores that SORBET isn’t just teaching facts—it’s empowering students to take ownership of social responsibility through reflective learning.
Academic Recognition
SORBET’s design and educational relevance earned it a Harvard Graduate School of Education case study spot for their Formative Evaluation for Educational Product Development course.(Corporate NTU) This distinction amplifies its status as a model of thoughtful, theory-grounded educational innovation with global reach.
Blog Structure with SORBET-Inspired Enhancements
Creation Process + NIE Influence
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Belief in Embodied Learning: Drawing from SORBET’s foundation, our simulation emphasizes experiential learning over abstraction. Students see dice rolls not as numbers, but decision-driven outcomes—mirroring how SORBET’s users experienced virus diffusion through virtual proximity and feedback.(ResearchGate, repository.nie.edu.sg)
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Dialogic Reflection Embedded: Just like SORBET’s two-part design—first, an immersive activity; second, structured discussion—we built in real-time feedback loops (e.g., probability charts and infection tables), encouraging learners to pause, reflect, and hypothesize.
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Multidisciplinary Potential: While our version is rooted in mathematics, like the NIE project, it’s primed for integration across subjects— geography (spatial diffusion), biology (epidemiology), or character education (responsibility).(repository.nie.edu.sg)
Educational Features (Sourced from NIE)
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Immediate Consequence Visualization: Inspired by SORBET’s lack of latency in feedback, our simulation updates infection stats in real-time, making students experiment and learn from immediate theoretical outcomes.(Corporate NTU)
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Learner Agency at the Helm: Mirroring SORBET’s emphasis on self-discipline and responsibility, our design foregrounds student choice—dice rolls, population size, and interaction impact all influence outcomes.(repository.nie.edu.sg)
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Immersive + Reflective Blend: Our simulation, like its predecessor, marries active simulation with structured analysis, fostering deeper conceptual understanding and grounded empathy.
Blog Enhancements for Virality
| Addition | Purpose |
|---|
| Real student impact data |
Builds credibility and emotional connection |
| Harvard recognition mention |
Elevates prestige and share appeal |
| Embodied learning highlights |
Sparks curiosity and resonance across educators |
| Multidisciplinary framing |
Broader audience applicability |
Final Revised Snippets:
Enhanced Overview:
Inspired by NIE’s award-winning SORBET Project—celebrated as a Harvard case study—this interactive simulator leverages embodied learning to transform abstract probability concepts into lived experiences with immediate visual and emotional impact.
Academic Credibility Section:
Rooted in rigorous educational research, SORBET’s immersive environment and thoughtful design earned recognition from both the Immersive Learning Research Network and the Harvard Graduate School of Education—setting a high bar for innovative ed‑tech.
Student Reflection/Empowerment Highlight:
In pilot studies, over 100 students reported feeling more personally responsible for public health outcomes after engaging with SORBET—showcasing how embodiment can drive mindset change.(ResearchGate, singteach.nie.edu.sg)
Create a comprehensive HTML application that combines dice probability with disease transmission simulation. The application should demonstrate how mathematical probability directly controls epidemiological outcomes.
## Core Requirements
### 1. Dice-Based Transmission System
- **Primary Feature**: Each contact between infected and healthy students must trigger an automatic dice roll
- **Transmission Probability**: Use formula (dice sum ÷ 12) where dice sum ranges from 2-12
- Roll of 2 = 16.7% transmission chance
- Roll of 6 = 50% transmission chance
- Roll of 12 = 100% transmission chance
- **Visual Dice**: Display two animated dice in bottom-left corner showing current roll values
- **No Fixed Rate**: Do NOT use a fixed infection rate slider - all transmission must be dice-controlled
### 2. Student Population Simulation
- **Population**: 50 students (configurable 10-200) represented as colored dots
- **Movement**: Students move randomly with physics-based boundary collision
- **States**:
- Healthy (teal/cyan with glow effect)
- Infected (red with pulsing animation and infection radius)
- Recovered (blue with glow effect)
- **Patient Zero**: Start with 1 infected student, rest healthy
- **Recovery System**: Infected students recover after configurable time (50-500 units)
### 3. User Interface Layout
- **Container**: 100% width, 450px height (iframe compatible)
- **Control Panel**: Top bar with all controls
- Play, Pause, Step, Reset buttons
- Population input field
- Recovery time slider
- Manual dice roll button
- Toggle checkboxes for: Infection Radius, Movement Vectors, Statistics
- **Main Area**: Split into simulation area (left 2/3) and charts (right 1/3)
- **Variables Panel**: Top-right overlay showing live statistics
### 4. Live Variables Panel (Top-Right)
Display real-time statistics:
- Time (seconds elapsed)
- Healthy count
- Infected count
- Recovered count
- Last Dice Roll (sum value)
- Transmission % (last roll ÷ 12 × 100)
- Total Contacts (proximity events)
- Successful Transmissions
- Overall Success Rate (transmissions ÷ contacts × 100)
### 5. Charts Area
**Dice Sum Distribution Chart:**
- Bar chart showing frequency of each dice sum (2-12)
- Bars should form bell curve over time
- Update after each roll
**Disease Progression Chart:**
- Three bars showing current Healthy/Infected/Recovered counts
- Color-coded: teal, red, blue
- Real-time height updates
### 6. Visual Effects & Feedback
- **Infection Radius**: Dashed circles around infected students
- **Transmission Success**: Brief scale-up animation when student gets infected
- **Dice Animation**: Rolling animation when dice are rolled
- **Color Coding**: Consistent throughout (teal=healthy, red=infected, blue=recovered)
### 7. Educational Integration
- **Probability Demonstration**: Students see how dice rolls directly affect disease spread
- **Statistics Tracking**: Real-time comparison of theoretical vs actual transmission rates
- **Visual Learning**: Clear connection between mathematical probability and biological outcomes
## Technical Specifications
### Styling Requirements
- **Theme**: Dark background with professional gradients
- **Colors**:
- Background: Dark blue gradients (#1a1a2e, #16213e)
- Healthy: #4ecdc4 (teal)
- Infected: #ff6b6b (red)
- Recovered: #45b7d1 (blue)
- **Responsive**: Works on desktop, tablet, mobile
- **No External Dependencies**: Pure HTML/CSS/JavaScript only
### Core JavaScript Logic
```javascript
// Key function structure needed:
class DiseaseSimulation {
rollDiceForTransmission() {
// Roll two dice, return sum
// Update dice display
// Calculate transmission probability (sum/12)
// Update statistics
}
updateSimulation() {
// Move students
// Check for contacts within infection radius
// For each contact: roll dice and determine transmission
// Handle recovery
// Update all displays
}
}
```
### Contact Detection & Transmission
- **Infection Radius**: 30 pixels around infected students
- **Contact Event**: When healthy student enters infected student's radius
- **Automatic Dice Roll**: Each contact triggers dice roll
- **Transmission Decision**: Compare random number to (dice sum ÷ 12)
- **Visual Feedback**: Show successful transmissions with animation
### Animation & Controls
- **Play/Pause**: Start/stop the simulation loop
- **Step**: Advance simulation by one frame
- **Reset**: Clear all and regenerate population
- **Manual Roll**: Allow users to roll dice independently for exploration
## Expected Behavior
When running:
1. Students move randomly around the simulation area
2. When infected student comes near healthy student, dice automatically roll
3. Dice values determine transmission probability
4. Statistics update in real-time showing the relationship between dice outcomes and disease spread
5. Charts show both dice distribution and disease progression
6. Students can observe how probability theory governs epidemic patterns
## Educational Outcomes
Students should be able to:
- See direct connection between dice probability and disease transmission
- Understand how individual random events create population-level patterns
- Observe the relationship between theoretical probability and actual outcomes
- Learn how mathematical concepts apply to real-world biological processes
## File Structure
Create a single HTML file containing all CSS and JavaScript inline for easy deployment and sharing.
The final result should be a professional, educational simulation that clearly demonstrates the integration of probability mathematics with epidemiological modeling.
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1. With Cline Bot
I need to analyze the user's task. They want me to create an HTML app that combines two different functionalities:
1. Rolling 2 dice, recording the sum, and displaying in a bar chart
2. Capturing student identities, infected students, and simulating disease spread
The requirements also specify:
- Realistic physical behavior simulation
- Scientific accuracy
- Visual & variable representation with a live variables display panel at top-right
- Interactive controls (Play, Pause, Step, Reset)
- Sliders, dropdowns, input fields for learning
- Checkboxes to toggle display elements
- Visual aids like arrows, lines, icons for vectors
- UI guidelines: controls at top, maximize screen space, fit in iframe (100% width, 450px height)
- Must be offline-compatible (no external libraries/CDNs)
- Cross-platform compatibility
- Performance optimization
This seems like a complex simulation that combines probability (dice rolling) with epidemiology (disease spread simulation). I should create a comprehensive HTML file that includes both functionalities in a cohesive interface.
Let me start by creating the HTML file with all the required features.
I'll create a comprehensive HTML app that combines dice rolling with disease spread simulation. Let me start by creating the main HTML file with all the required features.
I have successfully created a comprehensive HTML app that combines dice rolling with disease spread simulation. The application includes all requested features:
**Core Functionality:**
- Rolling of 2 dice with animated rolling effect
- Recording and displaying dice sum distribution in a bar chart
- Student population simulation with disease spread mechanics
- Real-time tracking of healthy, infected, and recovered students
**Scientific Accuracy & Behavior:**
- Realistic disease transmission based on proximity and infection rates
- Recovery system with configurable recovery time
- Population dynamics following epidemiological principles
- Physics-based movement with boundary collision detection
**Visual & Variable Representation:**
- Live variables panel in top-right corner showing real-time statistics
- Clear visual distinction between healthy (teal), infected (red pulsing), and recovered (blue) students
- Infection radius visualization with dashed circles
- Two bar charts: dice sum distribution and disease progression
**Interactive Controls:**
- Play, Pause, Step, and Reset buttons at the top
- Population size input (10-200 students)
- Infection rate slider (1-10%)
- Recovery time slider (50-500 time units)
- Roll Dice button for probability experiments
- Toggle checkboxes for infection radius, movement vectors, and statistics display
**Professional UI Design:**
- Dark theme with gradient backgrounds and professional styling
- Responsive design that works in both landscape and portrait orientations
- Optimized for iframe display (100% width, 450px height)
- No external dependencies - fully offline compatible
- Cross-platform compatibility for all modern browsers and devices
**Key Features Demonstrated:**
- Disease spreads realistically from infected to healthy students within infection radius
- Students move around the simulation area with physics-based collision detection
- Recovery system allows infected students to become immune (blue)
- Dice rolling provides probability distribution visualization
- All controls are functional and provide real-time feedback
- Visual aids include infection radius circles and color-coded student states
The simulation successfully combines probability theory (dice rolling) with epidemiological modeling, making it an excellent educational tool for understanding both statistical distributions and disease transmission dynamics.
Excellent idea! That's a perfect way to integrate the dice rolling with the disease transmission mechanics. Here's how this would work:
**Transmission Probability System:**
- When an infected student comes within infection radius of a healthy student, instead of using a fixed infection rate, we roll the dice
- The dice sum (2-12) divided by 12 gives us the transmission probability:
- Roll of 2 → 2/12 = 16.7% chance of infection
- Roll of 6 → 6/12 = 50% chance of infection
- Roll of 12 → 12/12 = 100% chance of infection
**Implementation Plan:**
1. **Remove the current infection rate slider** - replace it with dice-based probability
2. **Automatic dice rolling** - whenever a contact occurs, automatically roll dice to determine transmission
3. **Visual feedback** - show the dice roll result and whether infection occurred for each contact
4. **Statistics tracking** - track how many contacts occurred, successful transmissions, and the dice roll distribution
5. **Educational display** - show students the relationship between dice probability and infection outcomes
**Enhanced Educational Value:**
- Students can see how probability theory directly controls disease spread
- Demonstrates that lower dice rolls make epidemics spread slower
- Shows how random chance affects individual transmission events
- Creates a clear connection between mathematical probability and real-world disease dynamics
**Additional Features:**
- Option to manually roll dice to "force" transmission events
- Display showing "Last Contact: Dice rolled 8/12 = 67% → Infection occurred!"
- Running statistics of transmission success rate vs. expected probability
This would transform the simulation into a powerful tool showing how probability mathematics directly governs epidemiological outcomes. Students would see that each infection event is determined by chance (dice roll), but the overall epidemic pattern emerges from these individual probability events.
Should I proceed with implementing this dice-controlled transmission system? Would you like me to toggle to Act mode to make these modifications?
I have successfully integrated dice rolling with disease transmission probability, creating a powerful educational tool that demonstrates how probability mathematics directly controls epidemiological outcomes.
**Key Integration Achieved:**
**Dice-Based Transmission System:**
- Each contact between infected and healthy students triggers an automatic dice roll
- Transmission probability = (dice sum) / 12, ranging from 16.7% (roll of 2) to 100% (roll of 12)
- Removed the fixed infection rate slider and replaced it with dynamic dice-controlled transmission
**Enhanced Educational Features:**
- **Live Variables Panel**: Shows last dice roll, current transmission percentage, total contacts, successful transmissions, and overall success rate
- **Real-time Statistics**: Students can see how individual dice rolls affect the epidemic progression
- **Visual Feedback**: Students briefly scale up when successfully infected, providing immediate visual confirmation
- **Probability Distribution**: Dice chart shows the bell curve distribution of transmission probabilities over time
**Scientific Accuracy:**
- Each transmission event is now governed by probability theory (dice roll ÷ 12)
- Students can observe how random chance affects individual infections while overall patterns emerge from cumulative probability
- Success rate statistics demonstrate the relationship between theoretical probability and actual outcomes
**Educational Value:**
- Students see direct connection between dice probability (2/12 to 12/12) and disease transmission
- Demonstrates how mathematical probability governs real-world biological processes
- Shows that while individual events are random, population-level patterns follow predictable probability distributions
- Creates understanding of how chance events accumulate to create epidemic curves
**Working Features Demonstrated:**
- Automatic dice rolling for each contact event
- Manual dice rolling for exploration
- Real-time transmission percentage calculation
- Contact and transmission tracking
- Success rate analysis showing actual vs. expected probability outcomes
- Visual infection radius and recovery system
- Comprehensive statistics panel
The simulation now perfectly integrates probability theory with epidemiological modeling, making it an excellent educational tool for understanding how mathematical concepts directly influence biological and medical phenomena.
I have created a comprehensive recreation prompt that another LLM can use to rebuild this simulation from scratch. The prompt includes:
**Complete Technical Specifications:**
- Dice-based transmission system with (dice sum ÷ 12) probability formula
- Student population simulation with movement physics and three health states
- Professional UI layout with control panel, simulation area, and charts
- Live variables panel showing real-time statistics
- Visual effects and animations for educational feedback
**Detailed Requirements:**
- Exact color schemes and styling specifications
- JavaScript class structure and key functions
- Contact detection and transmission logic
- Animation and control system requirements
- Chart implementations for dice distribution and disease progression
**Educational Integration:**
- Clear connection between probability mathematics and biological outcomes
- Real-time statistics showing theoretical vs actual transmission rates
- Visual learning through dice rolls directly controlling disease spread
- Professional presentation suitable for educational environments
**Implementation Guidelines:**
- Single HTML file with inline CSS/JavaScript
- No external dependencies for offline compatibility
- Responsive design for multiple devices
- 450px height iframe compatibility
The prompt provides enough detail for another LLM to recreate the exact functionality we developed, including the key innovation of using dice rolls to determine transmission probability rather than fixed rates. This creates a powerful educational tool that demonstrates how mathematical probability directly governs epidemiological outcomes.
