流行病模型的 Python 代码示例，SEIR模型，直接写代码

好的，下面是一个关于 SEIR 流行病模型的 Python 代码示例：

import numpy as np

class SEIRModel:
    def __init__(self, beta, sigma, gamma, mu, initial_conditions):
        """
        beta: transmission rate 传染率
        sigma: incubation rate 潜伏期
        gamma: recovery rate 恢复率
        mu: mortality rate 死亡率
        initial_conditions: list of 4 elements, representing the initial number of
            susceptible, exposed, infected, and recovered individuals
        """
        self.beta = beta
        self.sigma = sigma
        self.gamma = gamma
        self.mu = mu
        self.S, self.E, self.I, self.R = initial_conditions
    
    def deriv(self, t, y):
        """
        Returns the derivative of the SEIR model at time t.
        """
        S, E, I, R = y
        N = S + E + I + R
        dSdt = -self.beta * S * I / N
        dEdt = self.beta * S * I / N - self.sigma * E
        dIdt = self.sigma * E - self.gamma * I - self.mu * I
        dRdt = self.gamma * I
        return dSdt, dEdt, dIdt, dRdt
    
    def step(self, t, dt):
        """
        Advances the model by dt time units.
        """
        y = self.S, self.E, self.I, self.R
        dydt = self.deriv(t, y)
        S_inc, E_inc, I_inc, R_inc = [x * dt for x in dydt]
        self.S += S_inc
        self.E += E_inc
        self.I += I_inc
        self.R += R_inc

# 初始条件
N = 1000  # population size
initial_conditions = [N - 1, 0, 1, 0]  # one infected individual

# 参数
beta = 0.25
sigma = 1.0
gamma = 0.1
mu = 0.01

# 创建模型
model = SEIRModel(beta, sigma, gamma, mu, initial_conditions)

# 模拟
t_max = 365  # run for one year
dt = 0.1  # time step
time_points = np.arange(0, t_max, dt)
S_history = []
E_history = []
I_history = []
R_history = []
for t in time_points:
    S_history.append(model.S)
    E_history.append(model.E)
    I_history.append(model.I)
    R_history.append(model.R)
    model.step(t,