This is a computational portfolio, not a forecasting system. Intervention scenarios are methodological demonstrations. They are not empirical estimates of vaccine or transmission-reduction effectiveness. The immunity extension is synthetic. It is not Finnish DIPP data and not clinical serology.
Models
The SIR model moves people from susceptible to infectious to recovered or removed. The SEIR model adds an exposed compartment, governed by σ. The baseline run uses a closed population of 5,000, with 10 people infectious at the start, over 180 days.
β and γ are modelling assumptions. σ is treated as an evidence-informed input, not a universal biological constant. Under the simplified SIR formulation, R₀ = β / γ = 1.5.
Scenarios and uncertainty
Four scenarios are compared: baseline, vaccination, transmission reduction, and the two together. One-way sensitivity analysis varies β and γ separately. A 1,000-run Monte Carlo, with a fixed random seed, reports percentile ranges. Those ranges are model-based. They are not statistical confidence intervals.
Immunity and the growth threshold
A later extension builds a synthetic longitudinal dataset: identifiers, birth year, sampling year, age group, birth cohort, synthetic enterovirus-type labels, synthetic antibody titres, an immunity class, and a susceptibility fraction. Mean susceptibility is summarised by calendar year and type, then passed into an immunity-adjusted SEIR model. That model looks at the effective reproduction number, early growth, the peak infectious population, the timing of the peak, and cumulative simulated infections.
With R₀ = 1.5, the critical susceptible fraction is 1 / R₀, about 66.7%. Below that, initial growth is not expected (R_eff < 1). At the threshold, R_eff = 1. Above it, initial growth is possible. This threshold is derived from the model. It has not been checked against a real epidemic.
What was checked
Computational checks cover the initial conditions, conservation of the population, non-negative compartments, the expected shape of the trajectories, and whether a rerun reproduces the same result. Those checks show internal consistency. They are not empirical validation.
Repository: Respiratory-Outbreak-Simulator.