EoN.nonMarkov_directed_percolate_network¶
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EoN.
nonMarkov_directed_percolate_network
(G, xi, zeta, transmission)[source]¶ 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]) determines whether u transmits to v.
returns True if transmission happens and False if it does not
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.