EoN.basic_discrete_SIS

EoN.basic_discrete_SIS(G, p, initial_infecteds=None, rho=None, tmin=0, tmax=100, *, rng=None, return_full_data=False, sim_kwargs=None)

Does a simulation of the simple case of all nodes transmitting with probability p independently to each susceptible neighbor and then recovering.

This is not directly described in Kiss, Miller, & Simon.

Arguments:

G networkx Graph

The network the disease will transmit through.

p number

transmission probability

initial_infecteds node or iterable of nodes

if a single node, then this node is initially infected

if an iterable, then whole set is initially infected

if None, then choose randomly based on rho.

If rho is also None, a random single node is chosen.

If both initial_infecteds and rho are assigned, then there is an error.

rho number

initial fraction infected. number is int(round(G.order()*rho))

rng random number generator

If None, will be set to np.random.default_rng()

return_full_data boolean (default False)

Tells whether a Simulation_Investigation object should be returned.

sim_kwargs keyword arguments

Any keyword arguments to be sent to the Simulation_Investigation object Only relevant if return_full_data=True

Returns:

if return_full_data is False

t, S, I

All numpy arrays

if return_full_data is True

full_data Simulation_Investigation object

from this we can extract the status history of all nodes We can also plot the network at given times and even create animations using class methods.

SAMPLE USE:

import networkx as nx
import EoN
import matplotlib.pyplot as plt
G = nx.fast_gnp_random_graph(1000,0.002)
t, S, I = EoN.basic_discrete_SIS(G, 0.6, tmax = 20)
plt.plot(t,S)