examples: add SIR epidemic model with Mesa 4.0 discrete space API

[B2prakash]

Title: add SIR epidemic model with Mesa 4.0 discrete space API`

Summary

Adds a new SIR (Susceptible-Infected-Recovered) epidemic model demonstrating disease spread on a 2D grid using Mesa 4.0's discrete space API.

Files

• model.py: SIR model using OrthogonalMooreGrid and CellAgent
• app.py: SolaraViz visualization with adjustable parameters
• Readme.md: Background, model description, parameters, how to run, results
• metadata.toml: Structured metadata for discoverability

What this demonstrates

• CellAgent for grid-based agents with state transitions
• OrthogonalMooreGrid with Moore neighborhood for spatial interaction
• DataCollector for tracking population-level metrics
• SolaraViz interactive visualization

GSoC contributor checklist

Context & motivation: I built this model as part of my GSoC preparation for the Behavioral Modeling Framework project. The SIR model is a natural fit for mesa-examples because it demonstrates state-based agent behavior — agents transition between states based on local interactions, which is a pattern common to many behavioral models.

What I learned: Building this model taught me Mesa 4.0's discrete space system — how CellAgent replaces the old mesa.Agent + manual grid placement pattern, how cell.neighborhood replaces get_neighborhood(), and how agent movement works through cell assignment rather than move_agent(). The biggest difference from Mesa 3.x is that agents "live in" cells rather than being "placed on" a grid.

Learning repo: https://github.com/B2prakash/GSoC-learning-space

Readiness checks:

• I have read the contributing guide
• I have performed a self-review
• Another GSoC contributor has reviewed this PR: @Vanya-kapoor
• Model runs without errors: python model.py
• Documentation included (Readme.md)