SEIR模型的Python代码

SEIR模型是一种常见的传染病模型，它将人群分为四个类别：易感者(S)，潜伏期感染者(E)，感染者(I)和康复者(R)。下面是SEIR模型的Python代码：

import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt

# Total population, N.
N = 10000
# Initial number of infected and recovered individuals, I0 and R0.
I0, R0 = 1, 0
# Everyone else, S0, is susceptible to infection initially.
S0 = N - I0 - R0
# Contact rate, beta, and mean recovery rate, gamma, (in 1/days).
beta, gamma = 0.2, 1./10 
# A time grid of days
t = np.linspace(0, 160, 160)

# The SEIR model differential equations.
def deriv(y, t, N, beta, gamma):
    S, E, I, R = y
    dSdt = -beta * S * I / N
    dEdt = beta * S * I / N - gamma * E
    dIdt = gamma * E - gamma * I
    dRdt = gamma * I
    return dSdt, dEdt, dIdt, dRdt

# Initial conditions vector
y0 = S0, I0, R0

# Integrate the SEIR equations over the time grid, t.
ret = odeint(deriv, y0, t, args=(N, beta, gamma))
S, E, I, R = ret.T

# Plot the data on three separate curves for S(t), E(t), I(t) and R(t)
fig = plt.figure(facecolor='w')
ax = fig.add_subplot(111, facecolor='#dddddd', axisbelow=True)
ax.plot(t, S/1000, 'b', alpha=0.5, lw=2, label='Susceptible')
ax.plot(t, E/1000, 'y', alpha=0.5, lw=2, label='Exposed')
ax.plot(t, I/1000, 'r', alpha=0.5, lw=2, label='Infected')
ax.plot(t, R/1000, 'g', alpha=0.5, lw=2, label='Recovered with immunity')
ax.set_xlabel('Time /days')
ax.set_ylabel('Number (1000s)')
ax.set_ylim(0,N/1000)
ax.yaxis.set_tick_params(length=0)
ax.xaxis.set_tick_params(length=0)
ax.grid(b=True, which='major', c='w', lw=2, ls='-')
legend = ax.legend()
legend.get_frame().set_alpha(0.5)
plt.show()

该代码使用SciPy库中的odeint函数求解微分方程，将结果绘制成图表展示出来。你可以根据实际需要调整参数或绘图方式，以满足不同的应用需求。