SimInf (version 8.2.0)

run: Run the SimInf stochastic simulation algorithm

Description

Run the SimInf stochastic simulation algorithm

Usage

run(model, ...)
# S4 method for SimInf_model
run(model, solver = c("ssm", "aem"), ...)
# S4 method for SEIR
run(model, solver = c("ssm", "aem"), ...)
# S4 method for SIR
run(model, solver = c("ssm", "aem"), ...)
# S4 method for SISe
run(model, solver = c("ssm", "aem"), ...)
# S4 method for SISe3
run(model, solver = c("ssm", "aem"), ...)
# S4 method for SISe3_sp
run(model, solver = c("ssm", "aem"), ...)
# S4 method for SISe_sp
run(model, solver = c("ssm", "aem"), ...)

Arguments

model

The SimInf model to run.

...

Additional arguments.

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)

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