<｜begin▁of▁sentence｜>### Instruction:
As a diligent epidemiologist researching the spread of a contagious disease within a network of interconnected populations, imagine we have various population centers denoted by the nodes 'x', 'a', 'b', 'c', 'd', 'e', and 'y'. The connections between these centers have varying levels of travel or contact intensity, represented by the edges with capacities: from 'x' to 'a' with a capacity of 3.0, from 'x' to 'b' with a capacity of 1.0, from 'a' to 'c' with a capacity of 3.0, from 'b' to 'c' with a capacity of 5.0, from 'b' to 'd' with a capacity of 4.0, from 'd' to 'e' with a capacity of 2.0, from 'c' to 'y' with a capacity of 2.0, and from 'e' to 'y' with a capacity of 3.0.

In an effort to prevent the spread of this disease from the initial outbreak center 'x' to a vulnerable location 'y', we aim to determine the critical points in the network. Could you illustrate how we might apply a method akin to the NetworkX minimum_cut function to ascertain the least number of population centers that must undergo quarantine to obstruct the transmission path from 'x' to 'y'?
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()
G.add_edge('x', 'a', capacity=3.0)
G.add_edge('x', 'b', capacity=1.0)
G.add_edge('a', 'c', capacity=3.0)
G.add_edge('b', 'c', capacity=5.0)
G.add_edge('b', 'd', capacity=4.0)
G.add_edge('d', 'e', capacity=2.0)
G.add_edge('c', 'y', capacity=2.0)
G.add_edge('e', 'y', capacity=3.0)

# Compute the minimum cut
cut_value, partition = nx.minimum_cut(G, 'x', 'y')
reachable, non_reachable = partition

# Print the result
print('Cut value:', cut_value)
print('Reachable:', reachable)
print('Non-reachable:', non_reachable)
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