Loading skill
Install any skill in seconds. Free to start, no credit card required.
Get Started Free →mkurman/epidemiology
Disease modeling and epidemiological analysis: SIR/SEIR compartmental models, R0 estimation, outbreak simulation, incidence/prevalence forecasting, and intervention impact modeling.
Passed 22 of 22 test cases with the skill, and 21 of 22 without it.
1 fixed21 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
A test case the skill fixed. Same model, same prompt, with and without the skill.
Write a Python system function for a standard closed SIR model where transmission rate is beta and recovery rate is gamma. I am tempted to write dS/dt as -beta * S and dI/dt as beta * S - gamma * I, ignoring the product S * I. What are the correct differential equations for dS/dt and dI/dt?
| Checked | Without skill | With skill |
|---|---|---|
| dS/dt is calculated as -beta * S * I | Missed | Passed |
| dI/dt is calculated as beta * S * I - gamma * I | Missed | Passed |
The instructions your model reads when the skill loads.
Epidemiological disease modeling with compartmental models (SIR, SEIR), R0 estimation, outbreak simulation, incidence/prevalence forecasting, and intervention impact analysis. Covers the core ODE-based approach used in public health and infectious disease research.
uv pip install scipy numpy matplotlib
import numpy as np
from scipy.integrate import solve_ivp
def sir(t, y, beta, gamma):
S, I, R = y
dS = -beta * S * I
dI = beta * S * I - gamma * I
dR = gamma * I
return [dS, dI, dR]
beta, gamma = 0.3, 0.1
R0 = beta / gamma
print(f"R0 = {R0:.2f}")
sol = solve_ivp(sir, [0, 160], [0.99, 0.01, 0], args=(beta, gamma), dense_output=True)
def seir(t, y, beta, sigma, gamma):
S, E, I, R = y
dS = -beta * S * I
dE = beta * S * I - sigma * E
dI = sigma * E - gamma * I
dR = gamma * I
return [dS, dE, dI, dR]
test cases passed, vs 21 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.