instruction stringlengths 93 136 | output stringlengths 236 370 | domain stringclasses 4
values | quality_score float64 1 1 |
|---|---|---|---|
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.78 et guerison gamma=0.17 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.78
gamma = 0.17
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 4.5... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.62 et guerison gamma=0.14 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.62
gamma = 0.14
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 4.4... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.26 et guerison gamma=0.18 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.26
gamma = 0.18
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 1.4... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.29 et guerison gamma=0.19 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.29
gamma = 0.19
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 1.5... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.23 et guerison gamma=0.16 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.23
gamma = 0.16
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 1.4... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.26 et guerison gamma=0.28 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.26
gamma = 0.28
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 0.9... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.45 et guerison gamma=0.1 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.45
gamma = 0.1
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 4.5"... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.73 et guerison gamma=0.13 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.73
gamma = 0.13
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 5.6... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.57 et guerison gamma=0.15 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.57
gamma = 0.15
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 3.8... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.78 et guerison gamma=0.27 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.78
gamma = 0.27
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 2.8... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.69 et guerison gamma=0.25 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.69
gamma = 0.25
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 2.7... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.45 et guerison gamma=0.27 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.45
gamma = 0.27
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 1.6... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.6 et guerison gamma=0.22 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.6
gamma = 0.22
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 2.73... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.2 et guerison gamma=0.19 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.2
gamma = 0.19
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 1.05... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.29 et guerison gamma=0.14 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.29
gamma = 0.14
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 2.0... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.61 et guerison gamma=0.06 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.61
gamma = 0.06
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 10.... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.48 et guerison gamma=0.28 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.48
gamma = 0.28
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 1.7... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.58 et guerison gamma=0.19 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.58
gamma = 0.19
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 3.0... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.23 et guerison gamma=0.19 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.23
gamma = 0.19
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 1.2... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.29 et guerison gamma=0.11 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.29
gamma = 0.11
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 2.6... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.21 et guerison gamma=0.17 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.21
gamma = 0.17
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 1.2... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.47 et guerison gamma=0.13 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.47
gamma = 0.13
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 3.6... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.67 et guerison gamma=0.2 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.67
gamma = 0.2
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 3.35... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.2 et guerison gamma=0.12 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.2
gamma = 0.12
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 1.67... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.5 et guerison gamma=0.26 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.5
gamma = 0.26
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 1.92... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.79 et guerison gamma=0.18 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.79
gamma = 0.18
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 4.3... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.45 et guerison gamma=0.25 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.45
gamma = 0.25
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 1.8... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.24 et guerison gamma=0.22 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.24
gamma = 0.22
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 1.0... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.46 et guerison gamma=0.23 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.46
gamma = 0.23
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 2.0... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.28 et guerison gamma=0.11 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.28
gamma = 0.11
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 2.5... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.65 et guerison gamma=0.21 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.65
gamma = 0.21
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 3.1... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.45 et guerison gamma=0.17 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.45
gamma = 0.17
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 2.6... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.61 et guerison gamma=0.16 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.61
gamma = 0.16
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 3.8... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.36 et guerison gamma=0.12 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.36
gamma = 0.12
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 3.0... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.33 et guerison gamma=0.2 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.33
gamma = 0.2
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 1.65... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.57 et guerison gamma=0.1 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.57
gamma = 0.1
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 5.7"... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.61 et guerison gamma=0.1 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.61
gamma = 0.1
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 6.1"... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.39 et guerison gamma=0.09 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.39
gamma = 0.09
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 4.3... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.31 et guerison gamma=0.12 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.31
gamma = 0.12
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 2.5... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.52 et guerison gamma=0.24 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.52
gamma = 0.24
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 2.1... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.77 et guerison gamma=0.11 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.77
gamma = 0.11
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 7.0... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.41 et guerison gamma=0.08 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.41
gamma = 0.08
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 5.1... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.69 et guerison gamma=0.21 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.69
gamma = 0.21
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 3.2... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.74 et guerison gamma=0.22 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.74
gamma = 0.22
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 3.3... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.2 et guerison gamma=0.17 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.2
gamma = 0.17
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 1.18... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.79 et guerison gamma=0.28 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.79
gamma = 0.28
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 2.8... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.26 et guerison gamma=0.18 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.26
gamma = 0.18
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 1.4... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.3 et guerison gamma=0.19 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.3
gamma = 0.19
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 1.58... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.54 et guerison gamma=0.13 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.54
gamma = 0.13
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 4.1... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.57 et guerison gamma=0.07 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.57
gamma = 0.07
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 8.1... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.45 et guerison gamma=0.07 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.45
gamma = 0.07
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 6.4... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.24 et guerison gamma=0.1 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.24
gamma = 0.1
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 2.4"... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.39 et guerison gamma=0.15 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.39
gamma = 0.15
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 2.6... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.28 et guerison gamma=0.17 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.28
gamma = 0.17
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 1.6... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.46 et guerison gamma=0.19 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.46
gamma = 0.19
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 2.4... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.33 et guerison gamma=0.25 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.33
gamma = 0.25
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 1.3... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.69 et guerison gamma=0.19 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.69
gamma = 0.19
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 3.6... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.72 et guerison gamma=0.18 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.72
gamma = 0.18
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 4.0... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.61 et guerison gamma=0.3 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.61
gamma = 0.3
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 2.03... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.75 et guerison gamma=0.17 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.75
gamma = 0.17
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 4.4... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.6 et guerison gamma=0.22 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.6
gamma = 0.22
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 2.73... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.38 et guerison gamma=0.2 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.38
gamma = 0.2
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 1.9"... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.28 et guerison gamma=0.11 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.28
gamma = 0.11
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 2.5... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.78 et guerison gamma=0.22 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.78
gamma = 0.22
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 3.5... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.55 et guerison gamma=0.27 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.55
gamma = 0.27
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 2.0... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.77 et guerison gamma=0.14 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.77
gamma = 0.14
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 5.5... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.23 et guerison gamma=0.17 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.23
gamma = 0.17
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 1.3... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.66 et guerison gamma=0.08 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.66
gamma = 0.08
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 8.2... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.69 et guerison gamma=0.16 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.69
gamma = 0.16
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 4.3... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.36 et guerison gamma=0.12 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.36
gamma = 0.12
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 3.0... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.41 et guerison gamma=0.2 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.41
gamma = 0.2
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 2.05... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.74 et guerison gamma=0.08 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.74
gamma = 0.08
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 9.2... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.65 et guerison gamma=0.24 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.65
gamma = 0.24
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 2.7... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.31 et guerison gamma=0.14 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.31
gamma = 0.14
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 2.2... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.66 et guerison gamma=0.08 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.66
gamma = 0.08
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 8.2... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.78 et guerison gamma=0.26 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.78
gamma = 0.26
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 3.0... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.75 et guerison gamma=0.22 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.75
gamma = 0.22
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 3.4... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.45 et guerison gamma=0.1 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.45
gamma = 0.1
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 4.5"... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.24 et guerison gamma=0.22 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.24
gamma = 0.22
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 1.0... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.62 et guerison gamma=0.14 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.62
gamma = 0.14
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 4.4... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.55 et guerison gamma=0.27 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.55
gamma = 0.27
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 2.0... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.34 et guerison gamma=0.24 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.34
gamma = 0.24
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 1.4... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.46 et guerison gamma=0.27 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.46
gamma = 0.27
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 1.7... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.22 et guerison gamma=0.29 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.22
gamma = 0.29
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 0.7... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.28 et guerison gamma=0.19 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.28
gamma = 0.19
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 1.4... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.59 et guerison gamma=0.09 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.59
gamma = 0.09
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 6.5... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.55 et guerison gamma=0.08 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.55
gamma = 0.08
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 6.8... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.7 et guerison gamma=0.2 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.7
gamma = 0.2
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 3.5")... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.49 et guerison gamma=0.24 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.49
gamma = 0.24
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 2.0... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.78 et guerison gamma=0.07 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.78
gamma = 0.07
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 11.... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.64 et guerison gamma=0.11 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.64
gamma = 0.11
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 5.8... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.79 et guerison gamma=0.11 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.79
gamma = 0.11
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 7.1... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.48 et guerison gamma=0.08 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.48
gamma = 0.08
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 6.0... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.67 et guerison gamma=0.29 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.67
gamma = 0.29
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 2.3... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.21 et guerison gamma=0.22 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.21
gamma = 0.22
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 0.9... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.25 et guerison gamma=0.28 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.25
gamma = 0.28
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 0.8... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.53 et guerison gamma=0.21 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.53
gamma = 0.21
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 2.5... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.64 et guerison gamma=0.29 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.64
gamma = 0.29
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 2.2... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.41 et guerison gamma=0.21 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.41
gamma = 0.21
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 1.9... | Systèmes Dynamiques & SciPy | 1 |
Simuler un modele epidemiologique SIR avec taux de transmission beta=0.46 et guerison gamma=0.09 via SciPy solve_ivp. | import numpy as np
from scipy.integrate import solve_ivp
beta = 0.46
gamma = 0.09
def sir(t, y):
S, I, R = y
N = S + I + R
return [-beta*S*I/N, beta*S*I/N - gamma*I, gamma*I]
t_span = (0, 100)
y0 = [999, 1, 0]
sol = solve_ivp(sir, t_span, y0, t_eval=np.linspace(0, 100, 100))
print(f"Taux R0 calculé: 5.1... | Systèmes Dynamiques & SciPy | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.