EoN.estimate_directed_SIR_prob_size¶

EoN.estimate_directed_SIR_prob_size(G, tau, gamma, *, rng=None)[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

Arguments:

G networkx Graph: The network the disease will transmit through.
tau positive float: transmission rate
gamma positive float: recovery rate
rng random number generator: If None, will be set to np.random.default_rng()

Returns:

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.

SAMPLE USE:

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)