R/icm.R

In EpiModel: Mathematical Modeling of Infectious Disease Dynamics

#' @title Stochastic Individual Contact Models
#'
#' @description Simulates stochastic individual contact epidemic models for
#'              infectious disease.
#'
#' @param param Model parameters, as an object of class [param.icm()].
#' @param init Initial conditions, as an object of class [init.icm()].
#' @param control Control settings, as an object of class
#'        [control.icm()].
#'
#' @details
#' Individual contact models are intended to be the stochastic microsimulation
#' analogs to deterministic compartmental models. ICMs simulate disease spread
#' on individual agents in discrete time as a function of processes with
#' stochastic variation. The stochasticity is inherent in all transition
#' processes: infection, recovery, and demographics.
#'
#' The `icm` function performs modeling of both the base model types
#' and original models. Base model types include one-group and two-group
#' models with disease types for Susceptible-Infected (SI),
#' Susceptible-Infected-Recovered (SIR), and Susceptible-Infected-Susceptible
#' (SIS). Original models may be built by writing new process modules that
#' either take the place of existing modules (for example, disease recovery),
#' or supplement the set of existing processes with a new one contained in an
#' original module.
#'
#' @return
#' A list of class `icm` with the following elements:
#'
#'  * **param:** the epidemic parameters passed into the model through
#'        `param`, with additional parameters added as necessary.
#'  * **control:** the control settings passed into the model through
#'        `control`, with additional controls added as necessary.
#'  * **epi:** a list of data frames, one for each epidemiological
#'        output from the model. Outputs for base models always include the
#'        size of each compartment, as well as flows in, out of, and between
#'        compartments.
#'
#'
#' @keywords model
#'
#' @seealso Extract the model results with [as.data.frame.icm()].
#' Summarize the time-specific model results with [summary.icm()].
#' Plot the model results with [plot.icm()]. Plot a compartment flow
#' diagram with [comp_plot()].
#'
#' @export
#'
#' @examples
#' \dontrun{
#' ## Example 1: SI Model
#' param <- param.icm(inf.prob = 0.2, act.rate = 0.25)
#' init <- init.icm(s.num = 500, i.num = 1)
#' control <- control.icm(type = "SI", nsteps = 500, nsims = 10)
#' mod1 <- icm(param, init, control)
#' mod1
#' plot(mod1)
#'
#' ## Example 2: SIR Model
#' param <- param.icm(inf.prob = 0.2, act.rate = 0.25, rec.rate = 1/50)
#' init <- init.icm(s.num = 500, i.num = 1, r.num = 0)
#' control <- control.icm(type = "SIR", nsteps = 500, nsims = 10)
#' mod2 <- icm(param, init, control)
#' mod2
#' plot(mod2)
#'
#' ## Example 3: SIS Model
#' param <- param.icm(inf.prob = 0.2, act.rate = 0.25, rec.rate = 1/50)
#' init <- init.icm(s.num = 500, i.num = 1)
#' control <- control.icm(type = "SIS", nsteps = 500, nsims = 10)
#' mod3 <- icm(param, init, control)
#' mod3
#' plot(mod3)
#'
#' ## Example 4: SI Model with Vital Dynamics (Two-Group)
#' param <- param.icm(inf.prob = 0.4,  inf.prob.g2 = 0.1,
#'                    act.rate = 0.25, balance = "g1",
#'                    a.rate = 1/100, a.rate.g2 = NA,
#'                    ds.rate = 1/100, ds.rate.g2 = 1/100,
#'                    di.rate = 1/50, di.rate.g2 = 1/50)
#' init <- init.icm(s.num = 500, i.num = 1,
#'                  s.num.g2 = 500, i.num.g2 = 0)
#' control <- control.icm(type = "SI", nsteps = 500, nsims = 10)
#' mod4 <- icm(param, init, control)
#' mod4
#' plot(mod4)
#' }
#'
icm <- function(param, init, control) {
  check.control.class("icm", "EpiModel icm")

  crosscheck.icm(param, init, control)
  verbose.icm(control, type = "startup")

  # Simulation loop start
  for (s in 1:control$nsims) {

    ## Initialization module
    if (!is.null(control[["initialize.FUN"]])) {
      dat <- do.call(control[["initialize.FUN"]], list(param, init, control))
    }


    # Timestep loop
    for (at in 2:control$nsteps) {

      ## Infection
      if (!is.null(control[["infection.FUN"]])) {
        dat <- do.call(control[["infection.FUN"]], list(dat, at))
      }


      ## Recovery
      if (!is.null(control[["recovery.FUN"]])) {
        dat <- do.call(control[["recovery.FUN"]], list(dat, at))
      }


      ## Departure Module
      if (!is.null(control[["departures.FUN"]])) {
        dat <- do.call(control[["departures.FUN"]], list(dat, at))
      }


      ## Arrival Module
      if (!is.null(control[["arrivals.FUN"]])) {
        dat <- do.call(control[["arrivals.FUN"]], list(dat, at))
      }


      ## Outputs
      if (!is.null(control[["prevalence.FUN"]])) {
        dat <- do.call(control[["prevalence.FUN"]], list(dat, at))
      }


      ## Track progress
      verbose.icm(dat, type = "progress", s, at)
    }

    # Set output
    if (s == 1) {
      out <- saveout.icm(dat, s)
    } else {
      out <- saveout.icm(dat, s, out)
    }



  } # Simulation loop end


  class(out) <- "icm"
  return(out)
}