The SWIR model was introduced in 2017 by Lee et al. .
In this model, during the epidemics, a node is allowed to change its status from Susceptible (S) to Weakened (W) or Infected (I), then to Removed (R).
The model is instantiated on a graph having a non-empty set of infected nodes.
At time t a node in the state I is selected randomly and the states of all neighbors are checked one by one. If the state of a neighbor is S then this state changes either i) to I with probability kappa or ii) to W with probability mu. If the state of a neighbor is W then the state W changes to I with probability nu. We repeat the above process for all nodes in state I and then changes to R for each associated node.
During the simulation a node can experience the following statuses:
|kappa||Model||float in [0, 1]||True|
|mu||Model||float in [0, 1]||True|
|nu||Model||float in [0, 1]||True|
The initial infection status can be defined via:
- percentage_infected: Model Parameter, float in [0, 1]
- Infected: Status Parameter, set of nodes
The two options are mutually exclusive and the latter takes precedence over the former.
The following class methods are made available to configure, describe and execute the simulation:
.. autoclass:: ndlib.models.epidemics.SWIRModel.SWIRModel
.. automethod:: ndlib.models.epidemics.SWIRModel.SWIRModel.__init__(graph)
.. automethod:: ndlib.models.epidemics.SWIRModel.SWIRModel.set_initial_status(self, configuration)
.. automethod:: ndlib.models.epidemics.SWIRModel.SWIRModel.reset(self)
.. automethod:: ndlib.models.epidemics.SWIRModel.SWIRModel.get_info(self)
.. automethod:: ndlib.models.epidemics.SWIRModel.SWIRModel.get_status_map(self)
.. automethod:: ndlib.models.epidemics.SWIRModel.SWIRModel.iteration(self)
.. automethod:: ndlib.models.epidemics.SWIRModel.SWIRModel.iteration_bunch(self, bunch_size)
In the code below is shown an example of instantiation and execution of an SEIR simulation on a random graph: we set the initial set of infected nodes as % of the overall population, a probability of infection of 1%, a removal probability of 0.5% and an incubation period of 5% (e.g. 20 iterations).
import networkx as nx import ndlib.models.ModelConfig as mc import ndlib.models.epidemics.SWIRModel as swir # Network topology g = nx.erdos_renyi_graph(1000, 0.1) # Model selection model = swir.SWIRModel(g) # Model Configuration cfg = mc.Configuration() cfg.add_model_parameter('kappa', 0.01) cfg.add_model_parameter('mu', 0.005) cfg.add_model_parameter('nu', 0.05) cfg.add_model_parameter("percentage_infected", 0.05) model.set_initial_status(cfg) # Simulation execution iterations = model.iteration_bunch(200)