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📊 SEIR Epidemic Model

Predictive Modeling for Healthcare Resource Planning

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💡 Toggle interventions during simulation to see real-time effects!

Disease Progression Over Time

Susceptible (S)
Exposed (E)
Infected (I)
Recovered (R)
95% Confidence Interval (MCMC)

😷 Masking Policy

Reduces transmission rate (β) by 50%

Effect on β: -50%
S → E reduction: 50%

❄️ Winter Seasonality

Indoor crowding increases transmission by 30%

Effect on β: +30%
Duration: Days 90-180

🦠 New Variant Emergence

More transmissible, partial immune escape

Transmission increase: +60%
Immune escape: 30% R → S
Emerges: Day 150
Peak Infections
0
Current Infected
0
Total Recovered
0
Hospital Beds Needed
0

🧮 Understanding the SEIR Model

The SEIR model divides the population into four compartments: Susceptible (S), Exposed (E), Infected (I), and Recovered (R).

This model helps hospitals predict when infection surges will occur, allowing them to: plan staffing levels, order supplies (PPE, ventilators), and prepare ICU capacity.

dS/dt = -β × S × I / N
dE/dt = β × S × I / N - σ × E
dI/dt = σ × E - γ × I
dR/dt = γ × I
β = transmission rate | σ = incubation rate | γ = recovery rate

Simulation Speed: Running at 10 days per second. Toggle interventions at any time to see how they change the disease trajectory in real-time!

Uncertainty Quantification: Click "Run with Uncertainty" to see prediction confidence intervals. The light red shaded area shows the 95% confidence interval for infections - the range where we expect the actual outcome to fall 95% of the time. The "NOW" line shows your current position in the simulation.