epi_trackers: Function to run the user-provided epi trackers

In EpiModel: Mathematical Modeling of Infectious Disease Dynamics

Function to run the user-provided epi trackers

Description

see the "Working with Custom Attributes and Summary Statistics in EpiModel" vignette.

Usage

epi_trackers(dat)

Arguments

dat	Main netsim_dat object containing a networkDynamic object and other initialization information passed from netsim.

Value

The updated netsim_dat main list object.

The tracker.list list

.tracker.list is a list of NAMED functions stored in the control list of the main netsim_dat class object.

Tracker Functions

This function will apply the tracker functions present in the control list .tracker.list. Each tracker must be a function with EXACTLY one argument: the netsim_dat main list object. They must return a VALUE of length one (numeric, logical or character).

See Also

netsim

Examples

## Not run: 

# Create some trackers
epi_prop_infected <- function(dat) {
  # we need two attributes for our calculation: `status` and `active`
  needed_attributes <- c("status", "active")
  # we use `with` to simplify code
  output <- with(EpiModel::get_attr_list(dat, needed_attributes), {
    pop <- active == 1             # we only look at active nodes
    cond <- status == "i"   # which are infected
    # how many are `infected` among the `active`
    sum(cond & pop, na.rm = TRUE) / sum(pop, na.rm = TRUE)
  })
  return(output)
}

epi_s_num <- function(dat) {
  needed_attributes <- c("status")
  output <- with(get_attr_list(dat, needed_attributes), {
    sum(status == "s", na.rm = TRUE)
  })
  return(output)
}

# Store the trackers in a named list. The names will be used as column names
# for in the `epi` list
some.trackers <- list(
  prop_infected = epi_prop_infected,
  s_num         = epi_s_num
)

# Make a simple SI model with custom trackers
control <- EpiModel::control.net(
  type = "SI",
  nsims = 1,
  nsteps = 50,
  verbose = FALSE,
  .tracker.list = some.trackers
)

param <- EpiModel::param.net(
  inf.prob = 0.3,
  act.rate = 0.1
)

nw <- network_initialize(n = 50)
nw <- set_vertex_attribute(nw, "race", rbinom(50, 1, 0.5))
est <- EpiModel::netest(
  nw,
  formation = ~edges,
  target.stats = 25,
  coef.diss = dissolution_coefs(~offset(edges), 10, 0),
  verbose = FALSE
)

init <- EpiModel::init.net(i.num = 10)
sim <- EpiModel::netsim(est, param, init, control)

d <- as.data.frame(sim)
d

## End(Not run)

EpiModel documentation built on July 9, 2023, 5:21 p.m.