Epidemic Simulation (SIR Model)

A discrete-time epidemic simulation modeling the spread of an infectious disease across a 150×150 grid representing New York City.
The simulator uses an SIR-style state model and probabilistic transitions to study how infection, recovery, and mortality evolve over time.

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Overview

This project simulates disease dynamics using a simplified but expressive epidemiological model.
Each cell in the grid represents a population unit and can exist in one of three states:

• S — Susceptible: healthy and vulnerable to infection
• I — Infected: currently infected and capable of spreading the disease
• R — Removed / Resistant: recovered or deceased and no longer susceptible

Time advances in discrete steps, allowing the simulation to track how local interactions and probabilistic events shape global outbreak behavior.

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Model Details

• Grid size: 150 × 150 (22,500 cells)
• State model: SIR (Susceptible → Infected → Removed)
• Infection spread:
  Infected cells may transmit disease to adjacent neighbors based on a configurable virality probability.
• Recovery:
  Infected cells recover after a fixed number of time steps.
• Mortality:
  Death probability follows a normal distribution, modeling time-dependent mortality risk.
• Simulation scale:
  Visualizes outbreak dynamics over hundreds of discrete time steps.

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Visualization

The simulation is visualized directly in Jupyter Notebook using matplotlib.

• Green cells represent susceptible populations
• Red cells represent infected populations
• Gray cells represent removed or resistant populations

This makes it easy to observe how parameter changes affect spread speed, peak infection, and total mortality.

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Files

• simulator.ipynb — Main Jupyter Notebook containing the full simulation, analysis, and visualization
• README.md — Project documentation
• requirements.txt — Minimal Python dependencies

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How to Run

1. Clone the repository:

   git clone https://github.com/tahalahlou/epidemic-simulation.git
   cd epidemic-simulation
   

2. (Optional) Install dependencies:

   pip install -r requirements.txt

3. Open the notebook:

   jupyter notebook simulator.ipynb

4. Run all cells top-to-bottom to execute the simulation and view visualizations