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.
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 withscipy.integrate.solve_ivpon slider change and redraw the S/I/R curves.Scope
I₀ / N.R₀ = β/γshown 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.