EoN.SIR_heterogeneous_meanfield(Sk0, Ik0, Rk0, tau, gamma, tmin=0, tmax=100, tcount=1001, return_full_data=False)[source]

Encodes System (5.11) of Kiss, Miller, & Simon. Please cite the book if using this algorithm.

In the text this is often referred to as the “heterogeneous mean-field model closed at the level of pairs”

This is also called Degree-baded Mean Field or Mean Field Social Heterogeneity

Ik0 and Rk0 are similar to Sk0.

[S_k] = [S_k](0) theta^k [I_k] = [N_k] - [S_k] - [R_k] [dot{R}_k] = gamma [I_k] pi_I = sum_k k[I_k]

Sk0 array
Sk0[k] is the number of nodes that are susceptible and have degree k (even if some degrees missing).
Ik0 array
as in Sk0
Rk0 array
as in Sk0
tau positive float
transmission rate
gamma number
recovery rate
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, S, I, R, Sk, Ik, Rk (the Xk are numpy 2D arrays)
if return_full_data is False:
times, S, I, R (all numpy arrays)
import networkx as nx
import EoN
Sk0 = [995, 995, 995, 995, 995]
Ik0 = [5, 5, 5, 5, 5]
Rk0 = [0,0,0,0,0]
tau = 1
gamma = 2
t, S, I, R = EoN.SIR_heterogeneous_meanfield(Sk0, Ik0, Rk0, tau, gamma,
                                                tmax = 10)