sir-epidemic-simulator-milad 1.0.0

A complete epidemic modeling suite with SIR/SEIR models, network simulation, ML prediction, and Streamlit dashboard

🦠 SIR Epidemic Simulator

A Complete Epidemic Modeling Suite with 5 Integrated Features

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📋 Table of Contents

• Overview
• Features
• Installation
• Quick Start
• Project Structure
• Usage Guide
• Testing
• Security
• Screenshots
• Documentation
• License

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🔭 Overview

The SIR Epidemic Simulator is a comprehensive epidemic modeling toolkit combining classical compartmental models with modern machine learning. It provides researchers, students, and public health professionals with tools to simulate, analyze, and predict epidemic dynamics.

Key Capabilities

Feature	Description
SIR Model	Basic Susceptible-Infected-Recovered dynamics
SEIR Model	Adds Exposed/incubation compartment
Network Simulation	Fake news spread on social graphs
Parameter Optimization	Fit models to real-world data
ML Prediction	Forecast future cases with XGBoost/Random Forest
Scenario Comparison	Evaluate quarantine vs vaccination

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🚀 Installation

Prerequisites

• Python 3.8 or higher
• pip package manager

Steps

# Clone repository
git clone https://github.com/miladrezanezhad/sir_simulator.git
cd sir_simulator

# Install dependencies
pip install -r requirements.txt

# Verify installation
python -c "import streamlit, numpy, pandas; print('✅ Success!')"

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🎮 Quick Start

Launch Web Dashboard (Recommended)

streamlit run user_interface/app.py

Run CLI

python user_interface/cli.py --beta 0.5 --gamma 0.2 --tmax 100

Run All Tests

python run_all_tests.py

Interactive Menu

python main.py

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📁 Project Structure

sir_simulator/
│
├── core_models/              # Core mathematical models
│   ├── sir_model.py          # Basic SIR implementation
│   ├── seir_model.py         # SEIR with exposed compartment
│   └── network_model.py      # Social network spread simulation
│
├── advanced_features/        # Advanced capabilities
│   ├── parameter_optimization.py  # Curve fitting
│   ├── ml_prediction.py           # ML forecasting
│   └── scenario_comparison.py     # Intervention analysis
│
├── user_interface/           # UI applications
│   ├── app.py                # Streamlit dashboard
│   └── cli.py                # Command-line interface
│
├── tests/                    # Test suite (57+ tests)
│   ├── test_seir.py          # SEIR model tests
│   ├── test_network.py       # Network simulation tests
│   ├── test_optimization.py  # Parameter optimization tests
│   ├── test_ml.py            # ML prediction tests
│   ├── test_scenarios.py     # Scenario comparison tests
│   └── security/             # Security test suite
│       ├── test_dos_attack.py
│       ├── test_memory_exhaustion.py
│       ├── test_unicode_attacks.py
│       └── test_xss_prevention.py
│
├── docs/                     # Documentation
│   └── notebook.ipynb        # Educational Jupyter notebook
│
├── screenshots/              # Application screenshots
│   ├── SIR Epidemic Model.png
│   ├── SEIR Model with Exposed Compartment.png
│   ├── Fake News Spread on Social Network.png
│   ├── Parameter Optimization.png
│   ├── Machine Learning Prediction.png
│   ├── Scenario Comparison.png
│   └── dashboard.png
│
├── .github/workflows/        # CI/CD pipelines
│   ├── test.yml              # Test automation
│   └── security.yml          # Security scan pipeline
│
├── config/                   # Configuration files
├── outputs/                  # CSV export folder
│
├── README.md                 # This file
├── TESTING.md                # Testing guide
├── SECURITY_TESTS.md         # Security testing guide
├── LICENSE                   # MIT License
├── requirements.txt     # Python dependencies
├── run_all_tests.py          # Master test runner
└── .gitignore

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📊 Usage Guide

1. SIR Model

from core_models.sir_model import run_sir_simulation

df = run_sir_simulation(
    beta=0.5, gamma=0.2,
    S0=990, I0=10, R0=0,
    t_max=100, steps=500
)

2. SEIR Model

from core_models.seir_model import run_seir_simulation

df = run_seir_simulation(
    beta=0.5, sigma=0.2, gamma=0.1,
    S0=990, E0=0, I0=10, R0=0,
    t_max=100, steps=500
)

3. Network Simulation

from core_models.network_model import SocialNetworkSimulator

sim = SocialNetworkSimulator(num_nodes=200, network_type='scale_free')
df = sim.simulate_spread(transmission_prob=0.4, recovery_prob=0.1)

4. Parameter Optimization

from advanced_features.parameter_optimization import ParameterOptimizer

optimizer = ParameterOptimizer(model_type='sir')
results = optimizer.fit(observed_data, t, [990, 10, 0])
print(f"β={results['beta']:.3f}, γ={results['gamma']:.3f}, R0={results['R0']:.3f}")

5. ML Prediction

from advanced_features.ml_prediction import EpidemicPredictor

predictor = EpidemicPredictor(model_type='random_forest')
metrics, predictions, _ = predictor.train(historical_data)
future = predictor.predict_future(historical_data, days=30)

6. Scenario Comparison

from advanced_features.scenario_comparison import ScenarioComparator

comp = ScenarioComparator()
scenarios, metrics = comp.compare_all_scenarios(days=120)
print(metrics)

---

🧪 Testing

Run all tests (35 unit tests + 22 security tests):

python run_all_tests.py
python -m unittest discover tests/security

Or run individual test files:

python tests/test_seir.py
python tests/test_network.py
python tests/test_optimization.py
python tests/test_ml.py
python tests/test_scenarios.py
python tests/security/test_dos_attack.py

Test Coverage

Module	Tests	Status
SEIR Model	7	✅
Network Model	10	✅
Parameter Optimization	6	✅
ML Prediction	2 (active) + 2 (skip)	✅
Scenario Comparison	8	✅
Security (DoS, Memory, Unicode, XSS)	22	✅
Total	57	✅ All Passing

For detailed testing documentation, see:

• Testing Guide - Complete testing documentation
• Security Testing Guide - Security test suite documentation

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🔒 Security

This project includes comprehensive security testing:

• ✅ DoS attack prevention
• ✅ Memory exhaustion protection
• ✅ Unicode/UTF-8 attack mitigation
• ✅ XSS prevention for Streamlit dashboard

All security tests pass with no vulnerabilities detected.

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📸 Screenshots

SIR Model	SEIR Model

Network Simulation	Parameter Optimization

ML Prediction	Scenario Comparison

📊 Streamlit Dashboard

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📚 Documentation

• Testing Guide - How to run and understand tests
• Security Testing Guide - Security test suite documentation
• Jupyter Notebook - Interactive educational notebook

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🔧 Requirements

numpy>=1.21.0
scipy>=1.7.0
pandas>=1.3.0
matplotlib>=3.4.0
streamlit>=1.20.0
networkx>=2.8
scikit-learn>=1.0.0
xgboost>=1.6.0

Install all dependencies with:

pip install -r requirements.txt

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🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Please ensure all tests pass before submitting:

python run_all_tests.py
python -m unittest discover tests/security

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🙏 Acknowledgments

• Classical SIR/SEIR models from Kermack-McKendrick theory
• Network science algorithms from NetworkX library
• Machine learning models from scikit-learn and XGBoost

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Built with ❤️ for epidemic modeling and public health research

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