Figure 4.1 (a and b)

Downloadable Source Code

import EoN
import networkx as nx
import matplotlib.pyplot as plt
import scipy
import random

N = 1000
kave = 30.
gamma = 1.
tau = 1./15
tmax = 10

rho = 0.05
iterations = 100

tcount = 50

report_times = scipy.linspace(0,tmax,tcount)

SIS_Isum=scipy.zeros(tcount)
SIR_Isum=scipy.zeros(tcount)

for counter in range(iterations):
    #do SIS simulation and then SIR simulation.
    G = nx.fast_gnp_random_graph(N, kave/(N-1))
    initial_infecteds = random.sample(G.nodes(), int(rho*G.order()))

    t, S, I = EoN.fast_SIS(G, tau, gamma, initial_infecteds=initial_infecteds, tmax=tmax)
    I = EoN.subsample(report_times, t, I)
    SIS_Isum += I
    plt.figure(0)
    plt.plot(report_times, I*1./N, linewidth=1, alpha=0.3, color='grey')
    t, S, I, R = EoN.fast_SIR(G, tau, gamma, initial_infecteds=initial_infecteds, tmax=tmax)
    I = EoN.subsample(report_times, t, I)
    SIR_Isum += I
    plt.figure(1)
    plt.plot(report_times, I*1./N, linewidth=1, alpha=0.3, color='grey')

S0 = (1-rho)*N
I0 = rho*N
SI0 = (1-rho)*N*kave*rho
SS0 = (1-rho)*N*kave*(1-rho)
t, S, I = EoN.SIS_homogeneous_pairwise(S0, I0, SI0, SS0, kave, tau,
                                        gamma, tmax=tmax)
plt.figure(0)
plt.plot(report_times,SIS_Isum/(N*iterations), color='k', linewidth=3)
plt.plot(t, I/N, '-.', color='k', linewidth=3)
plt.xlabel('$t$')
plt.ylabel('Prevalence')
plt.savefig('fig4p1a.png')


t, S, I, R = EoN.SIR_homogeneous_pairwise(S0, I0, 0, SI0, SS0, kave, tau,
                                        gamma, tmax=tmax)
plt.figure(1)
plt.plot(report_times,SIR_Isum/(N*iterations), color='k', linewidth=3)
plt.plot(t, I/N, '-.', color='k', linewidth=3)
plt.xlabel('$t$')
plt.ylabel('Prevalence')
plt.savefig('fig4p1b.png')