SimInf (version 9.0.0)

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"), ...)
# S4 method for SimInf_abc
run(model, ...)

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

S. Widgren, P. Bauer, R. Eriksson and S. Engblom. SimInf: An R Package for Data-Driven Stochastic Disease Spread Simulations. Journal of Statistical Software, 91(12), 1--42. 10.18637/jss.v091.i12. An updated version of this paper is available as a vignette in the package.

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

P. Bauer and S. Engblom. Sensitivity Estimation and Inverse Problems in Spatial Stochastic Models of Chemical Kinetics. In: A. Abdulle, S. Deparis, D. Kressner, F. Nobile and M. Picasso (eds.), Numerical Mathematics and Advanced Applications - ENUMATH 2013, pp. 519--527, Lecture Notes in Computational Science and Engineering, vol 103. Springer, Cham, 2015. 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)
# }