In this model, during the course of an epidemics, a node is allowed to change its status from Susceptible (S) to Infected (I), then to Removed (R).The dSIR implementation assumes that the process occurs on a directed/undirected dynamic network; this model was introduced by Milli et al. in 2018
The SIR model was introduced in 1927 by Kermack [1].
In this model, during the course of an epidemics, a node is allowed to change its status from Susceptible (S) to Infected(I), then to Removed (R).
The model is instantiated on a graph having a nonempty set of infected nodes.
SIR assumes that if, during a generic iteration, a susceptible node comes into contact with an infected one, it becomes infected with probability beta, than it can be switch to removed with probability gamma (the only transition allowed are S→I→R).
The dSIR implementation assumes that the process occurs on a directed/undirected dynamic network; this model was introduced by Milli et al. in 2018 [2].
During the simulation a node can experience the following statuses:
Name  Code 

Susceptible  0 
Infected  1 
Removed  2 
Name  Type  Value Type  Default  Mandatory  Description 

beta  Model  float in [0, 1]  True  Infection probability  
gamma  Model  float in [0, 1]  True  Removal probability 
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:
ndlib.models.dynamic.DynSIRModel.
DynSIRModel
(graph)Model Parameters to be specified via ModelConfig
Parameters: 


DynSIRModel.
__init__
(graph)Model Constructor
Parameters:  graph – A networkx graph object 

DynSIRModel.
set_initial_status
(self, configuration)Set the initial model configuration
Parameters:  configuration – a `ndlib.models.ModelConfig.Configuration` object 

DynSIRModel.
reset
(self)Reset the simulation setting the actual status to the initial configuration.
DynSIRModel.
get_info
(self)Describes the current model parameters (nodes, edges, status)
Returns:  a dictionary containing for each parameter class the values specified during model configuration 

DynSIRModel.
get_status_map
(self)Specify the statuses allowed by the model and their numeric code
Returns:  a dictionary (status>code) 

DynSIRModel.
iteration
(self)Execute a single model iteration
Returns:  Iteration_id, Incremental node status (dictionary node>status) 

DynSIRModel.
execute_snapshots
(bunch_size, node_status)DynSIRModel.
execute_iterations
(node_status)In the code below is shown an example of instantiation and execution of an DynSIR simulation on a dynamic random graph: we set the initial set of infected nodes as 5% of the overall population, a probability of infection of 1%, and a removal probability of 1%.
import networkx as nx
import dynetx as dn
import ndlib.models.ModelConfig as mc
import ndlib.models.dynamic.DynSIRModel as sir
from past.builtins import xrange
# Dynamic Network topology
dg = dn.DynGraph()
for t in xrange(0, 3):
g = nx.erdos_renyi_graph(200, 0.05)
dg.add_interactions_from(g.edges(), t)
# Model selection
model = sir.DynSIRModel(dg)
# Model Configuration
config = mc.Configuration()
config.add_model_parameter('beta', 0.01)
config.add_model_parameter('gamma', 0.01)
config.add_model_parameter("percentage_infected", 0.1)
model.set_initial_status(config)
# Simulate snapshot based execution
iterations = model.execute_snapshots()
# Simulation interaction graph based execution
iterations = model.execute_iterations()
[1] 

[2]  Letizia Milli, Giulio Rossetti, Fosca Giannotti, Dino Pedreschi. “Diffusive Phenomena in Dynamic Networks: a datadriven study”. Accepted to International Conference on Complex Networks (CompleNet), 2018, Boston. 
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