Pedagogy: add SIR parameter sliders (β, γ) to Spread_of_disease notebook

Add ipywidgets sliders for the SIR-model parameters β (transmission rate) and γ (recovery rate) — and optionally the initial infected fraction — to `50_ode/55_Spread_of_disease.ipynb`. Re-integrate with `scipy.integrate.solve_ivp` on slider change and redraw the S/I/R curves.

## Scope

- Sliders: β (e.g., 0–1), γ (e.g., 0–1), optionally `I₀ / N`.
- Plot: stacked or overlaid S(t), I(t), R(t) on the same axes.
- Optional callout: basic reproduction number `R₀ = β/γ` shown alongside, so the learner can directly link the parameter ratio to the wave shape.

## Why

SIR is a textbook 'parameter sensitivity' model — the entire teaching value is watching how the epidemic curve flattens, peaks, or never takes off as β/γ varies. Sliders are exactly the right interface for that intuition. Same validated pattern (iterative/parameter-driven model + ipywidgets) as #353 / #354.