global-actions {epiworldR}

Global Actions

Description

Global actions are functions that are executed at each time step of the simulation. They are useful for implementing interventions, such as vaccination, isolation, and social distancing by means of tools.

Usage

globalevent_tool(tool, prob, name = get_name_tool(tool), day = -99)

globalevent_tool_logit(
  tool,
  vars,
  coefs,
  name = get_name_tool(tool),
  day = -99
)

globalevent_set_params(
  param,
  value,
  name = paste0("Set ", param, " to ", value),
  day = -99
)

globalevent_fun(fun, name = deparse(substitute(fun)), day = -99)

add_globalevent(model, action)

Arguments

tool

An object of class tool.

prob

Numeric scalar. A probability between 0 and 1.

name

Character scalar. The name of the action.

day

Integer. The day (step) at which the action is executed (see details).

vars

Integer vector. The position of the variables in the model.

coefs

Numeric vector. The coefficients of the logistic regression.

param

Character scalar. The name of the parameter to be set.

value

Numeric scalar. The value of the parameter.

fun

Function. The function to be executed.

model

An object of class epiworld_model.

action

A global action.

Details

The function globalevent_tool_logit allows to specify a logistic regression model for the probability of using a tool. The model is specified by the vector of coefficients coefs and the vector of variables vars. vars is an integer vector indicating the position of the variables in the model.

The function globalevent_set_param allows to set a parameter of the model. The parameter is specified by its name param and the value by value.

The function globalevent_fun allows to specify a function to be executed at a given day. The function object must receive an object of class epiworld_model as only argument.

The function add_globalevent adds a global action to a model. The model checks for actions to be executed at each time step. If the added action matches the current time step, the action is executed. When day is negative, the action is executed at each time step. When day is positive, the action is executed at the specified time step.

Value

• The globalevent_set_params function returns an object of class epiworld_globalevent_set_param and epiworld_globalevent.

• globalevent_tool returns an object of class epiworld_globalevent_tool and epiworld_globalevent.

• globalevent_tool_logit returns an object of class epiworld_globalevent_tool_logit and epiworld_globalevent.

• The function add_globalevent returns the model with the added action.

See Also

epiworld-model

Examples

# Simple model
model_sirconn <- ModelSIRCONN(
  name                = "COVID-19",
  n                   = 10000,
  prevalence          = 0.01,
  contact_rate        = 5,
  transmission_rate   = 0.4,
  recovery_rate       = 0.95
)

# Creating a tool
epitool <- tool(
  name = "Vaccine",
  susceptibility_reduction = .9,
  transmission_reduction = .5,
  recovery_enhancer = .5, 
  death_reduction = .9
)


# Adding a global action
vaccine_day_20 <- globalevent_tool(epitool, .2, day = 20)
add_globalevent(model_sirconn, vaccine_day_20)

# Running and printing
run(model_sirconn, ndays = 40, seed = 1912)
model_sirconn
plot_incidence(model_sirconn)

# Example 2: Changing the contact rate -------------------------------------
model_sirconn2 <- ModelSIRCONN(
  name                = "COVID-19",
  n                   = 10000,
  prevalence          = 0.01,
  contact_rate        = 5,
  transmission_rate   = 0.4,
  recovery_rate       = 0.95
)

closure_day_10 <- globalevent_set_params("Contact rate", 0, day = 10)
add_globalevent(model_sirconn2, closure_day_10)

# Running and printing
run(model_sirconn2, ndays = 40, seed = 1912)
model_sirconn2
plot_incidence(model_sirconn2)
# Example using `globalevent_fun` to record the state of the
# agents at each time step.

# We start by creating an SIR connected model
model <- ModelSIRCONN(
  name              = "SIR with Global Saver",
  n                 = 1000,
  prevalence        = 0.01,
  contact_rate      = 5,
  transmission_rate = 0.4,
  recovery_rate     = 0.3
  )

# We create the object where the history of the agents will be stored
agents_history <- NULL

# This function prints the total number of agents in each state
# and stores the history of the agents in the object `agents_history`
hist_saver <- function(m) {
  
  message("Today's totals are: ", paste(get_today_total(m), collapse = ", "))

  # We use the `<<-` operator to assign the value to the global variable
  # `agents_history` (see ?"<<-")
  agents_history <<- cbind(
    agents_history,
    get_agents_states(m)
    )
    
}