EoN.directed_percolate_network(G, tau, gamma, weights=True)[source]

performs directed percolation, assuming that transmission and recovery are Markovian

From figure 6.13 of Kiss, Miller, & Simon. Please cite the book if using this algorithm.

This performs directed percolation corresponding to an SIR epidemic assuming that transmission is at rate tau and recovery at rate gamma

See Also:
nonMarkov_directed_percolate_network which allows for duration and
time to infect to come from other distributions.
nonMarkov_directed_percolate_network which allows for more complex
G networkx Graph
The network the disease will transmit through.
tau positive float
transmission rate
gamma positive float
recovery rate
weights boolean (default True)
if True, then includes information on time to recovery and delay to transmission. If False, just the directed graph.
H networkx DiGraph (directed graph)

a u->v edge exists in H if u would transmit to v if ever infected.

The edge has a time attribute (time_to_infect) which gives the delay from infection of u until transmission occurs.

Each node u has a time attribute (duration) which gives the duration of its infectious period.

import networkx as nx
import EoN

G = nx.fast_gnp_random_graph(1000,0.002)
H = EoN.directed_percolate_network(G, 2, 1)