Pandemic Simulator

Description

This project is a pandemic simulation application built using cellular automata to model the spread of infectious diseases. It enables users to configure parameters, run simulations, and visualize the dynamics of a pandemic over time in an interactive and user-friendly way.

The simulator models a population on a 2D grid where cells represent individuals in different states of health, such as:

• S: Susceptible
• E: Exposed
• I: Infected
• Q: Quarantined (isolated infected individuals)
• R: Recovered
• D: Deceased
• -1: Obstacles (non-interactive zones)

The application offers real-time animations of the simulation and supports statistical analysis of the disease progression.

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Features

1. Configurable Parameters:

   • Grid size (default: 100)
   • Disease Parameters:
     • Infection rate (p_inf, default: 0.2)
     • Incubation period (t_inc, default: 7 steps)
     • Recovery rate (p_rec, default: 0.05)
     • Death rate (p_dec, default: 0.01)
     • Recovery to susceptible period (t_il, default: 30 steps)
   • Disease Control:
     • Quarantine probability (p_quar, default: 0.1)
     • Mask usage parameters
   • Environment:
     • Obstacle ratio (default: 0.1)
   • Mask Usage:
     • Initial mask usage ratio (0-1, default: 0.0)
     • Dynamic mask usage toggle (0/1, default: 0)
     • Mask usage growth rate per step (default: 0.001)
     • Mask change start time (default: 50 steps)
   • Simulation:
     • Total steps (default: 200)
     • Animation interval (default: 30ms)

2. Visualization:

   • Interactive 2D animations of the grid
   • Line plots showing the number of infected, recovered, and deceased over time
   • Color-coded cells representing different health states

3. Social Distancing Measures:

   • Face Masks: Reduces transmission probability
     • Static or dynamic mask usage configuration
     • Initial mask usage ratio configurable (0-1)
     • Optional automatic increase in mask adoption over time
     • Masked susceptible individuals: 50% reduction in infection probability
     • Masked infected individuals: 70% reduction in transmission probability
     • Masks are randomly distributed among non-obstacle cells
     • Dynamic mask usage allows gradual increase with configurable:
       • Start time for mask usage changes
       • Growth rate per simulation step

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Technologies Used

• Programming Language: Python
• Libraries:
  • numpy: For numerical operations and matrix manipulations
  • matplotlib: For visualizations and animations

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Installation

Prerequisites

• Python 3.8 or higher
• Poetry package manager

Steps

1. Clone the repository:

   git clone git@github.com:KajSob/pandemic-spread-simulation.git
   cd pandemic-simulator
   

2. Install dependencies using Poetry:

    poetry install
   

3. Run the application:

   poetry run python pandemic_simulation/main.py