EoN.estimate_directed_SIR_prob_size(G, tau, gamma)[source]

Predicts probability and attack rate assuming continuous-time Markovian SIR disease on network G

From figure 6.17 of Kiss, Miller, & Simon. Please cite the book if using this algorithm

See Also:

estimate_nonMarkov_SIR_prob_size which handles nonMarkovian versions

G networkx Graph
The network the disease will transmit through.
tau positive float
transmission rate
gamma positive float
recovery rate
PE, AR numbers (between 0 and 1)
Estimates of epidemic probability and attack rate found by performing directed percolation, finding largest strongly connected component and finding its in/out components.
import networkx as nx
import EoN

G = nx.fast_gnp_random_graph(1000,0.003)
PE, AR = EoN.estimate_directed_SIR_prob_size(G, 2, 1)