sir-equations 0.1.1

Advanced epidemiological model combining SIR dynamics with social-psychological behavior patterns

Epidemiological Model with Social-Psychological Behavior

This package implements an advanced epidemiological modeling framework that combines traditional SIR (Susceptible-Infected-Recovered) dynamics with social-psychological behavior patterns based on General Adaptation Syndrome (GAS) theory. It's designed to provide deeper insights into disease transmission patterns by incorporating human behavioral responses during epidemics.

Features

Classical SIR Model

• Implementation of fundamental epidemiological equations
• Customizable infection and recovery rates
• Population conservation guarantees
• Numerical integration using advanced solvers
• Basic reproduction number (R0) calculations

Extended GAS Model Integration

## Three distinct behavioral states:

• Ignorance (Sign)

• Resistance (Sres)

• Exhaustion (Sexh)

• State transition modeling

• Behavioral feedback mechanisms

• Time-dependent adaptation patterns

Crowd Effect Modifications

• Superlinear alarm responses
• Population density considerations
• Social network effects
• Mass media influence factors
• Dynamic behavioral thresholds

## Advanced Analysis Tools

• Real-time simulation capabilities
• Data visualization utilities
• Parameter estimation functions
• Statistical analysis tools
• Cross-regional comparison frameworks

Installation

pip install sir-equations

Sample Code

from sir-equations import GASModel, run_simulation, analyze_data

Create a model instance

model = GASModel( a=0.3, # infection rate b=0.1, # recovery rate k=0.2, # crowd effect parameter Ip=0.02, # media influence factor transition_rates=[1/50, 1/100], # behavioral transition rates initial_conditions=[0.99, 0.01, 0] )

Run simulation

t, solution = run_simulation(model, t_span=100, t_points=1000)

Analyze results

results = analyze_data(t, solution, model_type='GAS')