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Get entities

Source: R/entity.R
entities.Rd

Entities in epiworld are objects that can contain agents.

Usage

get_entities(model)

# S3 method for class 'epiworld_entities'
x[i]

entity(name, prevalence, as_proportion, to_unassigned = TRUE)

get_entity_size(entity)

get_entity_name(entity)

entity_add_agent(entity, agent, model = attr(entity, "model"))

rm_entity(model, id)

add_entity(model, entity)

load_agents_entities_ties(model, agents_id, entities_id)

entity_get_agents(entity)

distribute_entity_randomly(prevalence, as_proportion, to_unassigned = TRUE)

distribute_entity_to_set(agents_ids)

set_distribution_entity(entity, distfun)

Arguments

model

Model object of class epiworld_model.

x

Object of class epiworld_entities.

i

Integer index.

name

Character scalar. Name of the entity.

prevalence

Numeric scalar. Prevalence of the entity.

as_proportion

Logical scalar. If TRUE, prevalence is interpreted as a proportion.

to_unassigned

Logical scalar. If TRUE, the entity is added to the unassigned pool.

entity

Entity object of class epiworld_entity.

agent

Agent object of class epiworld_agent.

id

Integer scalar. Entity id to remove (starting from zero).

agents_id

Integer vector.

entities_id

Integer vector.

agents_ids

Integer vector. Ids of the agents to distribute.

distfun

Distribution function object of class epiworld_distribution_entity.

Value

  • The function entity creates an entity object.

  • The function get_entity_size returns the number of agents in the entity.

  • The function get_entity_name returns the name of the entity.

  • The function entity_add_agent adds an agent to the entity.

  • The function rm_entity removes an entity from the model.

  • The function load_agents_entities_ties loads agents into entities.

  • The function entity_get_agents returns an integer vector with the agents in the entity (ids).

Details

Epiworld entities are especially useful for mixing models, particularly ModelSIRMixing and ModelSEIRMixing.

Examples

# Creating a mixing model
mymodel <- ModelSIRMixing(
 name = "My model",
 n = 10000,
 prevalence = .001,
 contact_rate = 10,
 transmission_rate = .1,
 recovery_rate = 1/7,
 contact_matrix = matrix(c(.9, .1, .1, .9), 2, 2)
)

ent1 <- entity("First", 5000, FALSE)
ent2 <- entity("Second", 5000, FALSE)

mymodel |>
  add_entity(ent1) |>
  add_entity(ent2)

run(mymodel, ndays = 100, seed = 1912)
#> _________________________________________________________________________
#> Running the model...
#> ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| done.
#>  done.

summary(mymodel)
#> ________________________________________________________________________________
#> ________________________________________________________________________________
#> SIMULATION STUDY
#> 
#> Name of the model   : Susceptible-Infected-Removed (SIR) with Mixing
#> Population size     : 10000
#> Agents' data        : (none)
#> Number of entities  : 2
#> Days (duration)     : 100 (of 100)
#> Number of viruses   : 1
#> Last run elapsed t  : 66.00ms
#> Last run speed      : 15.06 million agents x day / second
#> Rewiring            : off
#> 
#> Global events:
#>  - Update infected individuals (runs daily)
#> 
#> Virus(es):
#>  - My model
#> 
#> Tool(s):
#>  (none)
#> 
#> Model parameters:
#>  - Contact rate       : 10.0000
#>  - Prob. Recovery     : 0.1429
#>  - Prob. Transmission : 0.1000
#> 
#> Distribution of the population at time 100:
#>   - (0) Susceptible :  9990 -> 384
#>   - (1) Infected    :    10 -> 0
#>   - (2) Recovered   :     0 -> 9616
#> 
#> Transition Probabilities:
#>  - Susceptible  0.97  0.03  0.00
#>  - Infected     0.00  0.85  0.15
#>  - Recovered    0.00  0.00  1.00
#>