利用python预测sir模型_SI，SIS，SIR模型的正确实现（python）

我已经创建了上述模型的一些非常基本的实现。然而，尽管图表看起来是正确的，但是数字加起来并不是一个常数。这是每个隔间中易感/受感染/康复的人的总和，应该是N(总人数)，但它没有，因为某些原因，它加起来一些奇怪的十进制数，我真的不知道如何解决它，在看了3天之后。在

SI模型：import matplotlib.pyplot as plt

N = 1000000

S = N - 1

I = 1

beta = 0.6

sus = [] # infected compartment

inf = [] # susceptible compartment

prob = [] # probability of infection at time t

def infection(S, I, N):

t = 0

while (t < 100):

S = S - beta * ((S * I / N))

I = I + beta * ((S * I) / N)

p = beta * (I / N)

sus.append(S)

inf.append(I)

prob.append(p)

t = t + 1

infection(S, I, N)

figure = plt.figure()

figure.canvas.set_window_title('SI model')

figure.add_subplot(211)

inf_line, =plt.plot(inf, label='I(t)')

sus_line, = plt.plot(sus, label='S(t)')

plt.legend(handles=[inf_line, sus_line])

plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0)) # use scientific notation

ax = figure.add_subplot(212)

prob_line = plt.plot(prob, label='p(t)')

plt.legend(handles=prob_line)

type(ax) # matplotlib.axes._subplots.AxesSubplot

# manipulate

vals = ax.get_yticks()

ax.set_yticklabels(['{:3.2f}%'.format(x*100) for x in vals])

plt.xlabel('T')

plt.ylabel('p')

plt.show()

SIS型号：

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SIR型号：import matplotlib.pylab as plt

N = 1000000

S = N - 1

I = 1

R = 0

beta = 0.5

mu = 0.1

sus = []

inf = []

rec = []

def infection(S, I, R, N):

for t in range (1, 100):

S = S -(beta * S * I)/N

I = I + ((beta * S * I)/N) - R

R = mu * I

sus.append(S)

inf.append(I)

rec.append(R)

infection(S, I, R, N)

figure = plt.figure()

figure.canvas.set_window_title('SIR model')

inf_line, =plt.plot(inf, label='I(t)')

sus_line, = plt.plot(sus, label='S(t)')

rec_line, = plt.plot(rec, label='R(t)')

plt.legend(handles=[inf_line, sus_line, rec_line])

plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))

plt.xlabel('T')

plt.ylabel('N')

plt.show()