times2, S2, I2, R2 = EoN.fast_SIR(G,
tau1,
gamma,
initial_infecteds=infected,
initial_recovereds=recovered,
tmin=t1,
tmax=tmax)
plt.plot(times0, I0, label='fast_SIR')
plt.plot(
times1, I1
) #the first two have the same parameters, so the transition should be as if it were a single simulation
plt.plot(times2,
I2) #the infectiousness reduced, so a sharp change should be visible
times0, S0, I0, R0, infection_time, recovery_time = EoN.Gillespie_SIR(
G, tau0, gamma, rho=rho, tmax=t0, return_full_data=True)
infected, recovered = get_affected_nodes_at_end(infection_time, recovery_time)
times1, S1, I1, R1, infection_time, recovery_time = EoN.Gillespie_SIR(
G,
tau0,
gamma,
initial_infecteds=infected,
initial_recovereds=recovered,
tmin=t0,
tmax=t1,
return_full_data=True)
infected, recovered = get_affected_nodes_at_end(infection_time, recovery_time)
# writing a for loop to get simulations (mean and sd stored) for each graph in our list
nodes = [1000, 900, 800, 700, 600, 500, 400, 300, 200,
100] # for plot-divide by respective population
# 100 simulations for each graph
outbreak_list_random = []
for j, n in zip(range(len(immunised_graph_list_random)),
nodes): # for all percentage brackets
mean_outbreak_list = []
for g in immunised_graph_list_random[
j]: # for all graphs in one percentage bracket
inner_outbreaks = []
while len(inner_outbreaks) < 2000: # 2000 simulations for each graph
sim = eon.Gillespie_SIR(g, 0.95, 1)
inner_outbreaks.append((sim[1][0] - sim[1][-1]) /
n) # S(T) - S(0) (Outbreak Size definition)
mean_outbreak_list.append(inner_outbreaks)
outbreak_list_random.append(mean_outbreak_list)
# (1) FOR DEGREE CENTRALITY - just change the first line in the inner loop for others
final_node_immunise_list = []
for j in range(len(graph_list2)): # for all percentage brackets
inner_immunise_list = []
for i in range(len(
graph_list2[j])): # for all graphs in one percentage bracket
dc = nx.degree_centrality(graph_list2[j][i])
dc = pd.DataFrame([dc.keys(), dc.values()]).T
dc.columns = ['node', 'centrality value']
