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Load agents to a model

Source: R/agents.R, R/tool.R
agents_smallworld.Rd

These functions provide access to the network of the model. The network is represented by an edgelist. The agents_smallworld function generates a small world network with the Watts-Strogatz algorithm. The agents_from_edgelist function loads a network from an edgelist. The get_network function returns the edgelist of the network.

Usage

agents_smallworld(model, n, k, d, p)

agents_from_edgelist(model, source, target, size, directed)

get_network(model)

get_agents_states(model)

add_virus_agent(agent, model, virus, state_new = -99, queue = -99)

add_tool_agent(agent, model, tool, state_new = -99, queue = -99)

has_virus(agent, virus)

has_tool(agent, tool)

change_state(agent, model, state_new, queue = -99)

get_agents_tools(model)

Arguments

model

Model object of class epiworld_model.

n, size

Number of individuals in the population.

k

Number of ties in the small world network.

d, directed

Logical scalar. Whether the graph is directed or not.

p

Probability of rewiring.

source, target

Integer vectors describing the source and target of in the edgelist.

agent

Agent object of class epiworld_agent.

virus

Virus object of class epiworld_virus.

state_new

Integer scalar. New state of the agent after the action is executed.

queue

Integer scalar. Change in the queuing system after the action is executed.

tool

Tool object of class epiworld_tool.

Value

  • The 'agents_smallworld' function returns a model with the agents loaded.

  • The agents_from_edgelist function returns an empty model of class epiworld_model.

  • The get_network function returns a data frame with two columns (source and target) describing the edgelist of the network.

  • get_agents_states returns an character vector with the states of the agents by the end of the simulation.

  • The function add_virus_agent adds a virus to an agent and returns the agent invisibly.

  • The function add_tool_agent adds a tool to an agent and returns the agent invisibly.

  • The functions has_virus and has_tool return a logical scalar indicating whether the agent has the virus/tool or not.

  • get_agents_tools returns a list of class epiworld_agents_tools with epiworld_tools (list of lists).

Details

The new_state and queue parameters are optional. If they are not provided, the agent will be updated with the default values of the virus/tool.

Examples


# Initializing SIR model with agents_smallworld
sir <- ModelSIR(name = "COVID-19", prevalence = 0.01, transmission_rate = 0.9, 
                recovery_rate = 0.1)
agents_smallworld(
   sir,
   n = 1000, 
   k = 5,
   d = FALSE,
   p = .01
 )
run(sir, ndays = 100, seed = 1912)
#> _________________________________________________________________________
#> Running the model...
#> ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| done.
#>  done.
sir
#> ________________________________________________________________________________
#> Susceptible-Infected-Recovered (SIR)
#> It features 1000 agents, 1 virus(es), and 0 tool(s).
#> The model has 3 states.
#> The final distribution is: 0 Susceptible, 9 Infected, and 991 Recovered.

# We can also retrieve the network
net <- get_network(sir)
head(net)
#>   from  to
#> 1    0   1
#> 2    0   2
#> 3    0 998
#> 4    0 999
#> 5    1   2
#> 6    1   3

# Simulating a bernoulli graph
set.seed(333)
n <- 1000
g <- matrix(runif(n ^ 2) < .01, nrow = n)
diag(g) <- FALSE
el <- which(g, arr.ind = TRUE) - 1L


# Generating an empty model
sir <- ModelSIR("COVID-19", .01, .8, .3)
agents_from_edgelist(
  sir,
  source = el[,1],
  target = el[,2],
  size   = n,
  directed = TRUE
)

# Running the simulation
run(sir, 50)
#> _________________________________________________________________________
#> |Running the model...
#> |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| done.
#> | done.

plot(sir)