EoN.discrete_SIR(G, test_transmission=<function _simple_test_transmission_>, args=(), initial_infecteds=None, initial_recovereds=None, rho=None, tmin=0, tmax=inf, return_full_data=False, sim_kwargs=None)[source]

Simulates an SIR epidemic on G in discrete time, allowing user-specified transmission rules

From figure 6.8 of Kiss, Miller, & Simon. Please cite the book if using this algorithm.

Return details of epidemic curve from a discrete time simulation.

It assumes that individuals are infected for exactly one unit of time and then recover with immunity.

This is defined to handle a user-defined function test_transmission(node1,node2,*args) which determines whether transmission occurs.

So elaborate rules can be created as desired by the user.

By default it uses _simple_test_transmission_ in which case args should be entered as (p,)

G NetworkX Graph (or some other structure which quacks like a
NetworkX Graph)

The network on which the epidemic will be simulated.

test_transmission function(u,v,*args)

(see below for args definition) A function that determines whether u transmits to v. It returns True if transmission happens and False otherwise. The default will return True with probability p, where args=(p,)

This function can be user-defined. It is called like: test_transmission(u,v,*args) Note that if args is not entered, then args=(), and this call is equivalent to test_transmission(u,v)

args a list or tuple

The arguments of test_transmission coming after the nodes. If simply having transmission with probability p it should be entered as args=(p,)

[note the comma is needed to tell Python that this is really a tuple]

initial_infecteds node or iterable of nodes (default None)
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.
initial_recovereds as for initial_infecteds, but initially
recovered nodes.
rho number (default is None)

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

The default results in a single randomly chosen initial infection.

tmin start time

tmax stop time (default Infinity).

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

t, S, I, R numpy arrays

Or if return_full_data is True returns

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.
import networkx as nx
import EoN
import matplotlib.pyplot as plt
G = nx.fast_gnp_random_graph(1000,0.002)
t, S, I, R = EoN.discrete_SIR(G, args = (0.6,),

Because this sample uses the defaults, it is equivalent to a call to basic_discrete_SIR