EoN.SIR_heterogeneous_meanfield_from_graph(G, tau, gamma, initial_infecteds=None, initial_recovereds=None, rho=None, tmin=0, tmax=100, tcount=1001, return_full_data=False)[source]

Calls SIR_heterogeneous_meanfield after calculating Sk0, Ik0, Rk0 based on a graph G and initial fraction infected rho.

G networkx Graph
the contact network
tau positive float
transmission rate
gamma number
recovery rate
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 iterable of nodes (default None)
this whole collection is made recovered. Currently there is no test for consistency with initial_infecteds. Understood that everyone who isn’t infected or recovered initially is initially susceptible.
rho float between 0 and 1 (default None)
the fraction to be randomly infected at time 0 If None, then rho=1/N is used where N = G.order()
tmin number (default 0)
minimum report time
tmax number (default 100)
maximum report time
tcount integer (default 1001)
number of reports
return_full_data boolean
tells whether to just return times, S, I, R or all calculated data.
if return_full_data is True
times, Sk, Ik, Rk (the Xk are numpy 2D arrays)
if False,
times, S, I, R (all numpy arrays)
import networkx as nx
import EoN
G = nx.configuration_model([1,2,3,4]*1000)
tau = 1
gamma = 2
t, S, I, R = EoN.SIR_heterogeneous_meanfield_from_graph(G, tau, gamma,
                                                        tmax = 10)