dcm: Deterministic Compartmental Models

In EpiModel: Mathematical Modeling of Infectious Disease Dynamics

Deterministic Compartmental Models

Description

Solves deterministic compartmental epidemic models for infectious disease.

Usage

dcm(param, init, control)

Arguments

param	Model parameters, as an object of class param.dcm.
init	Initial conditions, as an object of class init.dcm.
control	Control settings, as an object of class control.dcm.

Details

The dcm function uses the ordinary differential equation solver in the deSolve package to model disease as a deterministic compartmental system. The parameterization for these models follows the standard approach in EpiModel, with epidemic parameters, initial conditions, and control settings.

The dcm function performs modeling of both base model types and original models with new structures. 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). Both base and original models require the param, init, and control inputs.

Value

A list of class dcm 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.

References

Soetaert K, Petzoldt T, Setzer W. Solving Differential Equations in R: Package deSolve. Journal of Statistical Software. 2010; 33(9): 1-25. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v033.i09")}.

See Also

Extract the model results with as.data.frame.dcm. Summarize the time-specific model results with summary.dcm. Plot the model results with plot.dcm. Plot a compartment flow diagram with comp_plot.

Examples

## Example 1: SI Model (One-Group)
# Set parameters
param <- param.dcm(inf.prob = 0.2, act.rate = 0.25)
init <- init.dcm(s.num = 500, i.num = 1)
control <- control.dcm(type = "SI", nsteps = 500)
mod1 <- dcm(param, init, control)
mod1
plot(mod1)

## Example 2: SIR Model with Vital Dynamics (One-Group)
param <- param.dcm(inf.prob = 0.2, act.rate = 5,
                   rec.rate = 1/3, a.rate = 1/90, ds.rate = 1/100,
                   di.rate = 1/35, dr.rate = 1/100)
init <- init.dcm(s.num = 500, i.num = 1, r.num = 0)
control <- control.dcm(type = "SIR", nsteps = 500)
mod2 <- dcm(param, init, control)
mod2
plot(mod2)

## Example 3: SIS Model with act.rate Sensitivity Parameter
param <- param.dcm(inf.prob = 0.2, act.rate = seq(0.1, 0.5, 0.1),
                   rec.rate = 1/50)
init <- init.dcm(s.num = 500, i.num = 1)
control <- control.dcm(type = "SIS", nsteps = 500)
mod3 <- dcm(param, init, control)
mod3
plot(mod3)

## Example 4: SI Model with Vital Dynamics (Two-Group)
param <- param.dcm(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.dcm(s.num = 500, i.num = 1,
                 s.num.g2 = 500, i.num.g2 = 0)
control <- control.dcm(type = "SI", nsteps = 500)
mod4 <- dcm(param, init, control)
mod4
plot(mod4)

EpiModel documentation built on Oct. 12, 2024, 1:06 a.m.