Outbreak Lab

2,000 people commute between homes, workplaces, schools and markets in four districts. A pathogen spreads whenever an infectious person stands close to a susceptible one. Change the rules and watch the curve bend, then compare with the textbook mean-field model.

Seed

Same seed + same settings = identical epidemic.

Scenarios

Pick a scenario to load its parameters and restart.

Pathogen

Interventions apply live

Auto-lockdown when hospitals fill (>60% beds)

Display

Infectious halos Quarantine rings Mean-field ODE overlay District borders

Event timeline

–Nothing yet.

How it works

S → E → I → R or D. Exposed people are latent for the incubation period; infectious people can spread the disease until they recover or die. Durations are Erlang-distributed, not fixed.
Per tick (30 simulated minutes) each infectious person can infect susceptible neighbours within a short radius (found with a spatial hash grid). The base rate is calibrated on a dry-run day so that the slider R₀ matches an average infectious person in a fully open city.
Masks cut outgoing (55%) and incoming (35%) risk, distancing thins out contacts away from home, vaccines make a share of people fully immune, testing isolates detected cases, and contact tracing quarantines their household and recent contacts. Compliance decides how many people actually follow the rules.
Lockdown sends workers home and closes schools; travel sets how often commuters cross into another district. Severe cases need a hospital bed; when beds run out, their risk of dying rises.
The dashed lines are a well-mixed ODE SEIR model driven by the same sliders. It ignores geography and households, so the gap between the two shows what space and structure change.

A teaching toy, not a forecast. Age mix: 20% children, 62% adults, 18% elders, with fatality scaled by age.

Epidemic curve Areavs ODE

Reproduction number Rt

Hospital load

Cumulative deaths

Epidemic curve

Reproduction number R_t