EPIDEMIC SPREAD

An epidemic burns out before it reaches everybody, and that is not luck. The SIR model of Kermack and McKendrick, published in 1927, splits a population into three compartments: susceptible people become infectious at rate β and then recover at rate γ, so the ratio R₀ = β/γ decides everything. Below 1 the chains of transmission break by themselves; above 1 the wave climbs to a peak and falls again as susceptibles run out. It is a deliberately simplified model assuming uniform mixing and lasting immunity, not a forecast of any real outbreak.

▸ Spawning the agent population (S · I · R)…
▸ Seeding patient zero
▸ Loading parameters β (transmission) · γ (recovery)
▸ Computing the basic reproduction number R₀ = β/γ
▸ Calibrating the epidemic curve
▸ Ready — Online. ✅
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⌂ Complex systems

Simulation room Epidemic spread

Epidemic · SIR model
Online
S → I → R · R₀ = β/γ
Epidemic state
🦠 Đang ủ
ring: 🟦 S · 🟥 I · ⬜ R
Susceptible S—
Infectious I—
Recovered/immune R—
Reproduction number R₀—
Epidemic peak (highest I)—
Cumulative cases—
The idea
No one "controls" the epidemic — it EMERGES from countless personal contacts. The epidemic flares when each case infects >1 person (R₀>1), and fades on its own when enough people are immune (herd immunity).
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🟦 susceptible · 🟥 infectious (red ring = spread range) · ⬜ immune · hover to "distance" people (push them apart)
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Epidemic curve (S · I · R over time) SIR
About Epidemic (SIR)

An epidemic burns out before it reaches everybody, and that is not luck. The SIR model of Kermack and McKendrick, published in 1927, splits a population into three compartments: susceptible people become infectious at rate β and then recover at rate γ, so the ratio R₀ = β/γ decides everything. Below 1 the chains of transmission break by themselves; above 1 the wave climbs to a peak and falls again as susceptibles run out. It is a deliberately simplified model assuming uniform mixing and lasting immunity, not a forecast of any real outbreak.

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