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🦠 SIR Epidemic Simulator Wiki

Welcome to the official documentation wiki for the SIR Epidemic Simulator.

This wiki provides detailed documentation, tutorials, and examples that go beyond the main README.

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📚 Quick Links

Page	Description
Home	You are here
Getting Started	Installation and first simulation
SIR Model Tutorial	Complete guide to SIR model
SEIR Model Tutorial	Complete guide to SEIR model
Network Simulation Tutorial	Social network spread simulation
ML Prediction Tutorial	Machine learning forecasting
Scenario Comparison Tutorial	Compare intervention strategies
FAQ	Frequently asked questions
API Reference	Complete function documentation
Contributing Guide	How to contribute

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🎯 What Can You Find Here?

For Beginners

• Step-by-step installation guides
• First simulation walkthrough
• Understanding epidemic models

For Researchers

• Mathematical background of SIR/SEIR models
• Parameter optimization techniques
• Customizing simulations for real data

For Developers

• Project structure explained
• Adding new features
• Running and writing tests

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🚀 Quick Start

Install the package

pip install sir-epidemic

Run CLI

sir-simulator --beta 0.5 --gamma 0.2 --tmax 100

Run Streamlit Dashboard

streamlit run src/sir_simulator/user_interface/app.py

Run Interactive Menu

python main.py

Run All Tests

python run_all_tests.py

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📊 Sample Code

Basic SIR Simulation

from sir_simulator.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
)
print(df.head())

Network Spread Simulation

from sir_simulator.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)

Parameter Optimization

from sir_simulator.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}")

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🔗 External Resources

• GitHub Repository
• PyPI Package
• Issue Tracker
• Discussions

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📝 License

This project is licensed under the MIT License.

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Need help? Open an issue or start a discussion.

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