EoN.SIS_individual_based_pure_IC

EoN.SIS_individual_based_pure_IC(G, tau, gamma, initial_infecteds, nodelist=None, tmin=0, tmax=100, tcount=1001, transmission_weight=None, recovery_weight=None, return_full_data=False)[source]

Encodes System (3.7) of Kiss, Miller, & Simon. Please cite the book if using this algorithm.

The difference between this and SIS_individual_based is that this one assumes a “pure initial condition”, that is, we know exactly what the statuses of the nodes are at the initial time.

<dot{Y}_i> = tau sum_j g_{ij} (1-<Y_i>)<Y_j> - gamma_i <Y_i>

Arguments:

G networkx Graph

The contact network

tau positive float

transmission rate of disease

gamma number (default None)

global recovery rate

initial_infecteds list or set

the set of nodes initially infected

nodelist list (default None)

list of nodes in G in desired order. (only matters if return_full_data==True)

tmin number (default 0)

minimum report time

tmax number (default 100)

maximum report time

tcount integer (default 1001)

number of reports

transmission_weight string (default None)

the label for a weight given to the edges. G.edge[i][j][transmission_weight] = g_{ij}

recovery_weight string (default None)

a label for a weight given to the nodes to scale their recovery rates

gamma_i = G.nodes[i][recovery_weight]*gamma

return_full_data boolean (default False)

Returns:

if return_full_data is True,

returns times, Ss, Is

if return_full_data is False,

returns times, S, I

SAMPLE USE:

import networkx as nx
import EoN
import matplotlib.pyplot as plt

G = nx.configuration_model([3,10]*1000)
nodelist = G.nodes()
initial_infecteds = range(100)
t, S, I = EoN.SIS_individual_based(G, 0.3, 1, initial_infecteds, nodelist,
                                    tmax = 20)
plt.plot(t,I)