EpiModel (version 2.5.0)

netsim: Stochastic Network Models

Description

Simulates stochastic network epidemic models for infectious disease.

Usage

netsim(x, param, init, control)

Value

A list of class netsim 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.

• stats: a list containing two sublists, nwstats for any network statistics saved in the simulation, and transmat for the transmission matrix saved in the simulation. See control.net for further details.

• network: a list of lists of networkDynamic or networkLite objects, with one list of objects for each model simulation.

If control$raw.output == TRUE: A list of the raw (pre-processed) netsim_dat objects, for use in simulation continuation.

Arguments

x

If control$start == 1, either a fitted network model object of class netest or a list of such objects. If control$start > 1, an object of class netsim. When multiple networks are used, the node sets (including network size and nodal attributes) are assumed to be the same for all networks.

param

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

init

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

control

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

Details

Stochastic network models explicitly represent phenomena within and across edges (pairs of nodes that remain connected) over time. This enables edges to have duration, allowing for repeated transmission-related acts within the same dyad, specification of edge formation and dissolution rates, control over the temporal sequencing of multiple edges, and specification of network-level features. A detailed description of these models, along with examples, is found in the Network Modeling for Epidemics course materials.

The netsim 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 parameterized 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 a new module. This functionality is documented in the Extending EpiModel section of the Network Modeling for Epidemics course materials. The list of modules within netsim available for modification is listed in modules.net.

References

Jenness SM, Goodreau SM and Morris M. EpiModel: An R Package for Mathematical Modeling of Infectious Disease over Networks. Journal of Statistical Software. 2018; 84(8): 1-47.

See Also

Extract the model results with as.data.frame.netsim. Summarize the time-specific model results with summary.netsim. Plot the model results with plot.netsim.

Examples

if (FALSE) {
## Example 1: SI Model without Network Feedback
# Network model estimation
nw <- network_initialize(n = 100)
nw <- set_vertex_attribute(nw, "group", rep(1:2, each = 50))
formation <- ~edges
target.stats <- 50
coef.diss <- dissolution_coefs(dissolution = ~offset(edges), duration = 20)
est1 <- netest(nw, formation, target.stats, coef.diss, verbose = FALSE)

# Epidemic model
param <- param.net(inf.prob = 0.3, inf.prob.g2 = 0.15)
init <- init.net(i.num = 10, i.num.g2 = 10)
control <- control.net(type = "SI", nsteps = 100, nsims = 5, verbose.int = 0)
mod1 <- netsim(est1, param, init, control)

# Print, plot, and summarize the results
mod1
plot(mod1)
summary(mod1, at = 50)

## Example 2: SIR Model with Network Feedback
# Recalculate dissolution coefficient with departure rate
coef.diss <- dissolution_coefs(dissolution = ~offset(edges), duration = 20,
                               d.rate = 0.0021)

# Reestimate the model with new coefficient
est2 <- netest(nw, formation, target.stats, coef.diss)

# Reset parameters to include demographic rates
param <- param.net(inf.prob = 0.3, inf.prob.g2 = 0.15,
                   rec.rate = 0.02, rec.rate.g2 = 0.02,
                   a.rate = 0.002, a.rate.g2 = NA,
                   ds.rate = 0.001, ds.rate.g2 = 0.001,
                   di.rate = 0.001, di.rate.g2 = 0.001,
                   dr.rate = 0.001, dr.rate.g2 = 0.001)
init <- init.net(i.num = 10, i.num.g2 = 10,
                 r.num = 0, r.num.g2 = 0)
control <- control.net(type = "SIR", nsteps = 100, nsims = 5,
                       resimulate.network = TRUE, tergmLite = TRUE)

# Simulate the model with new network fit
mod2 <- netsim(est2, param, init, control)

# Print, plot, and summarize the results
mod2
plot(mod2)
summary(mod2, at = 40)
}