Count Down compartments are used to evaluate time related deterministic events attached to network nodes.
Consider the transition rule Susceptible->Infected that has an latent period of t iterations.
Such rule can be described by a simple compartment that models Count Down behaviors. Let’s call il CD.
The rule will take as input the initial node status (Susceptible), the final one (Infected) and the CD compartment. CD will thus require a countdown name (cn) and the number of iterations (t) before activation.
During each rule evaluation, given a node n
- if the actual status of n equals the rule initial one
- if the node does not have an associated countdown cn initialize it to t
- if cn(t) > t decrement cn(t)
- if cn(t) <= t then CD is considered satisfied and the status of n changes from initial to final.
|name||string||None||True||Count Down name|
In the code below is shown the formulation of a model using CountDown compartments.
The compartment, c1, is used to implement the transition rule Susceptible->Infected. It requires activates after 10 iteration.
import networkx as nx import ndlib.models.ModelConfig as mc import ndlib.models.CompositeModel as gc import ndlib.models.compartments.CountDown as cd # Network generation g = nx.erdos_renyi_graph(1000, 0.1) # Composite Model instantiation model = gc.CompositeModel(g) # Model statuses model.add_status("Susceptible") model.add_status("Infected") # Compartment definition c1 = cd.CountDown("incubation", iterations=10) # Rule definition model.add_rule("Susceptible", "Infected", c1) # Model initial status configuration config = mc.Configuration() config.add_model_parameter('fraction_infected', 0.1) # Simulation execution model.set_initial_status(config) iterations = model.iteration_bunch(100)