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Stochastic Individual Contact Models

Source: R/icm.R
icm.Rd

Simulates stochastic individual contact epidemic models for infectious disease.

Usage

icm(param, init, control)

Arguments

param

Model parameters, as an object of class param.icm.

init

Initial conditions, as an object of class init.icm.

control

Control settings, as an object of class control.icm.

Value

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.

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.

See also

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.

Examples

if (FALSE) { # \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)
} # }