SimInf (version 6.2.0)

run: Run the SimInf stochastic simulation algorithm

Description

Run the SimInf stochastic simulation algorithm

Usage

run(model, threads = NULL, solver = c("ssm", "aem"))
# S4 method for SimInf_model
run(model, threads = NULL, solver = c("ssm",
  "aem"))

Arguments

model

The siminf model to run.

threads

Number of threads. Default is NULL, i.e. to use all available processors.

solver

Which numerical solver to utilize. Default is 'ssm'.

Value

SimInf_model object with result from simulation.

References

• Bauer P, Engblom S, Widgren S (2016) "Fast Event-Based Epidemiological Simulations on National Scales" International Journal of High Performance Computing Applications, 30(4), 438-453. doi:10.1177/1094342016635723

• Bauer P., Engblom S. (2015) Sensitivity Estimation and Inverse Problems in Spatial Stochastic Models of Chemical Kinetics. In: Abdulle A., Deparis S., Kressner D., Nobile F., Picasso M. (eds) Numerical Mathematics and Advanced Applications - ENUMATH 2013. Lecture Notes in Computational Science and Engineering, vol 103. Springer, Cham. Doi: 10.1007/978-3-319-10705-9_51

Examples

# NOT RUN {
## Create an 'SIR' model with 10 nodes and initialise
## it to run over 100 days.
model <- SIR(u0 = data.frame(S = rep(99, 10),
                             I = rep(1, 10),
                             R = rep(0, 10)),
             tspan = 1:100,
             beta = 0.16,
             gamma = 0.077)

## Run the model and save the result.
result <- run(model, threads = 1)

## Plot the proportion of susceptible, infected and recovered
## individuals.
plot(result)
# }