SEIS (DT)¶
In the SEIS model, during the course of an epidemics, a node is allowed to change its status from Susceptible (S) to Exposed (E) to Infected (I), then again to Susceptible (S).
The model is instantiated on a graph having a non-empty set of infected nodes.
SEIS assumes that if, during a generic iteration, a susceptible node comes into contact with an infected one, it becomes infected after an exposition period with probability beta, than it can switch back to susceptible with probability lambda (the only transition allowed are S→E→I→S).
This implementation assumes discrete time dynamics for the E->I and I->S transitions.
Statuses¶
During the simulation a node can experience the following statuses:
Name | Code |
---|---|
Susceptible | 0 |
Infected | 1 |
Exposed | 2 |
Parameters¶
Name | Type | Value Type | Default | Mandatory | Description |
---|---|---|---|---|---|
beta | Model | float in [0, 1] | True | Infection probability | |
lambda | Model | float in [0, 1] | True | Removal probability | |
alpha | Model | float in [0, 1] | True | Latent period |
The initial infection status can be defined via:
- fraction_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.
Example¶
In the code below is shown an example of instantiation and execution of an SEIS simulation on a random graph: we set the initial set of infected nodes as 5% of the overall population, a probability of infection of 1%, a removal probability of 0.5% and an latent period of 5% (e.g. 20 iterations).
import networkx as nx
import ndlib.models.ModelConfig as mc
import ndlib.models.epidemics as ep
# Network topology
g = nx.erdos_renyi_graph(1000, 0.1)
# Model selection
model = ep.SEISModel(g)
# Model Configuration
cfg = mc.Configuration()
cfg.add_model_parameter('beta', 0.01)
cfg.add_model_parameter('lambda', 0.005)
cfg.add_model_parameter('alpha', 0.05)
cfg.add_model_parameter("fraction_infected", 0.05)
model.set_initial_status(cfg)
# Simulation execution
iterations = model.iteration_bunch(200)