def simulate_sis_on_network(G):
# Model Configuration
cfg = mc.Configuration()
cfg.add_model_parameter('beta', 0.05)
cfg.add_model_parameter('lambda', 0.05)
cfg.add_model_initial_configuration("Infected", random.sample(G.nodes,
k=5))
# Model selection
model = ep.SISModel(G)
# Initialize the simulation
model.set_initial_status(cfg)
Si, Ii = [], []
for s in range(SIMULCOUNT):
# Reset the model
model.reset()
# Simulation execution
iterations = model.iteration_bunch(ITERCOUNT)
# Collect results for figures
S, I = [], []
for i in range(ITERCOUNT):
iteration = iterations[i]['node_count']
S.append(iteration[0] / G.number_of_nodes())
I.append(iteration[1] / G.number_of_nodes())
Si.append(S)
Ii.append(I)
plot_spreading_figures(Si, Ii)
def test_sis_model(self):
for g in get_graph(True):
model = epd.SISModel(g)
config = mc.Configuration()
config.add_model_parameter('beta', 0.5)
config.add_model_parameter('lambda', 0.2)
config.add_model_parameter("fraction_infected", 0.1)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
iterations = model.iteration_bunch(10, node_status=False)
self.assertEqual(len(iterations), 10)
def test_sis_model(self):
g = nx.erdos_renyi_graph(1000, 0.1)
model = epd.SISModel(g)
config = mc.Configuration()
config.add_model_parameter('beta', 0.5)
config.add_model_parameter('lambda', 0.2)
config.add_model_parameter("percentage_infected", 0.1)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
iterations = model.iteration_bunch(10, node_status=False)
self.assertEqual(len(iterations), 10)
def test_initial_infected(self):
for g in get_graph(True):
model = epd.SISModel(g)
config = mc.Configuration()
config.add_model_parameter('beta', 0.5)
config.add_model_parameter('lambda', 0.2)
predefined_infected = [0, 1, 2, 3, 4, 5]
config.add_model_initial_configuration("Infected", predefined_infected)
model.set_initial_status(config)
inft = [k for k, v in future.utils.iteritems(model.status) if v == 1]
self.assertAlmostEqual(inft, predefined_infected)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
import networkx as nx
import ndlib.models.ModelConfig as mc
import ndlib.models.epidemics as ep
from bokeh.io import output_notebook, show
from ndlib.viz.bokeh.DiffusionTrend import DiffusionTrend
# Network topology
g = nx.erdos_renyi_graph(1000, 0.05)
# Model selection
model = ep.SISModel(g)
# Model Configuration
cfg = mc.Configuration()
cfg.add_model_parameter('beta', 0.01)
cfg.add_model_parameter('proability of return to S stage', 0.005)
cfg.add_model_parameter("initial infected rate", 0.05)
model.set_initial_status(cfg)
# Simulation execution
iterations = model.iteration_bunch(200)
trends = model.build_trends(iterations)
#visualize
viz = DiffusionTrend(model, trends)
p = viz.plot(width=550, height=550)
show(p)
# plot SIS results
from bokeh.io import show, output_notebook
from ndlib.viz.bokeh.DiffusionTrend import DiffusionTrend
# %%
# to display SIS results plot inline in noteboook
output_notebook()
# %% [markdown]
# ### a) Plot fractions of infected and susceptible nodes in terms of time
# for infection rate $\beta = 0.1$ and recovery rate $\mu = 1$
# %%
# Model selection
model = ep.SISModel(graph)
# Model Configuration
cfg = mc.Configuration()
cfg.add_model_parameter('beta', 0.2)
cfg.add_model_parameter('lambda', 1)
cfg.add_model_parameter("fraction_infected", 0.05)
model.set_initial_status(cfg)
# Simulation execution
iterations = model.iteration_bunch(200)
trends = model.build_trends(iterations)
# plot fractions of infected and susceptible nodes in terms of time
viz = DiffusionTrend(model, trends)
p = viz.plot(width=600, height=600)
return cfg
if __name__ == '__main__':
g = nx.erdos_renyi_graph(1000, 0.1)
num_iterations = int(input('Number of iterations for all models: ')) # 200
###############################################################
SIModel = ep.SIModel(g.copy())
SIModel.set_initial_status(get_si_params())
SI_iterations = SIModel.iteration_bunch(num_iterations)
SI_trends = SIModel.build_trends(SI_iterations)
visualize(SIModel, SI_trends, sub_dir='epidemics')
SISModel = ep.SISModel(g.copy())
SISModel.set_initial_status(get_sis_params())
SIS_iterations = SISModel.iteration_bunch(num_iterations)
SIS_trends = SISModel.build_trends(SIS_iterations)
visualize(SISModel, SIS_trends, sub_dir='epidemics')
SEISModel = ep.SEISModel(g.copy())
SEISModel.set_initial_status(get_seis_params())
SEIS_iterations = SEISModel.iteration_bunch(num_iterations)
SEIS_trends = SEISModel.build_trends(SEIS_iterations)
visualize(SEISModel, SEIS_trends, sub_dir='epidemics')
SEIRModel = ep.SEIRModel(g.copy())
SEIRModel.set_initial_status(get_seir_params())
SEIR_iterations = SEIRModel.iteration_bunch(num_iterations)
SEIR_trends = SEIRModel.build_trends(SEIR_iterations)
