我认为，对于您选择的参数，您会遇到刚度问题-
由于数值的不稳定性，求解器的步长在求解曲线的斜率实际上很浅的区域变得越来越小。
lsoda由包裹的Fortran求解器
scipy.integrate.odeint尝试在适用于“刚性”和“非刚性”系统的方法之间进行自适应切换，但是在这种情况下，它似乎无法转换为刚性方法。
非常粗略地讲，您可以大幅增加允许的最大步数，而求解器将最终达到目标：
SIR = spi.odeint(eq_system, PopIn, t_interval,mxstep=5000000)
更好的选择是使用面向对象的ODE求解器
scipy.integrate.ode，它使您可以显式选择是使用刚性方法还是非刚性方法：
import numpy as npfrom pylab import *import scipy.integrate as spidef run(): #Parameter Values S0 = 99. I0 = 1. R0 = 0. PopIn= (S0, I0, R0) beta= 0.50 gamma=1/10. mu = 1/25550. t_end = 15000. t_start = 1. t_step = 1. t_interval = np.arange(t_start, t_end, t_step) #Solving the differential equation. Solves over t for initial conditions PopIn def eq_system(t,PopIn): '''Defining SIR System of Equations''' #Creating an array of equations Eqs= np.zeros((3)) Eqs[0]= -beta * (PopIn[0]*PopIn[1]/(PopIn[0]+PopIn[1]+PopIn[2])) - mu*PopIn[0] + mu*(PopIn[0]+PopIn[1]+PopIn[2]) Eqs[1]= (beta * (PopIn[0]*PopIn[1]/(PopIn[0]+PopIn[1]+PopIn[2])) - gamma*PopIn[1] - mu*PopIn[1]) Eqs[2]= gamma*PopIn[1] - mu*PopIn[2] return Eqs ode = spi.ode(eq_system) # BDF method suited to stiff systems of ODEs ode.set_integrator('vode',nsteps=500,method='bdf') ode.set_initial_value(PopIn,t_start) ts = [] ys = [] while ode.successful() and ode.t < t_end: ode.integrate(ode.t + t_step) ts.append(ode.t) ys.append(ode.y) t = np.vstack(ts) s,i,r = np.vstack(ys).T fig,ax = subplots(1,1) ax.hold(True) ax.plot(t,s,label='Susceptible') ax.plot(t,i,label='Infected') ax.plot(t,r,label='Recovered') ax.set_xlim(t_start,t_end) ax.set_ylim(0,100) ax.set_xlabel('Time') ax.set_ylabel('Percent') ax.legend(loc=0,fancybox=True) return t,s,i,r,fig,ax
