EoN.nonMarkov_directed_percolate_network

EoN.nonMarkov_directed_percolate_network(G, xi, zeta, transmission, *, rng=None)

performs directed percolation on a network following user-specified rules.

From figure 6.18 of Kiss, Miller, & Simon. Please cite the book if using this algorithm.

This algorithm is particularly intended for a case where the duration and delays from infection to transmission are somehow related to one another.

Warning:

You probably shouldn’t use this. Check if nonMarkov_directed_percolate_with_timing fits your needs better.

See Also:

nonMarkov_directed_percolate_network_with_timing

if your rule for creating the percolated network is based on calculating a recovery time for each node and then calculating a separate transmission time for the edges this will be better.

directed_percolate_network

if it’s just a constant transmission and recovery rate.

Arguments:

G networkx Graph

The input graph

xi dict

xi[u] gives all necessary information to determine what us infectiousness is.

zeta dict

zeta[v] gives everything needed about vs susceptibility

transmission user-defined function

transmission(xi[u], zeta[v]) or transmission(xi[u], zeta[v], rng) determines whether u transmits to v.

returns True if transmission happens and False if it does not

rng random number generator

If None, will be set to np.random.default_rng()

Returns:

H networkx DiGraph (directed graph)

Edge (u,v) exists in H if disease will transmit given the opportunity.

SAMPLE USE:

for now, I’m being lazy. Look at the sample for estimate_nonMarkov_SIR_prob_size to infer it.