SimInf (version 9.8.1)

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 SIS
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, ...)

Value

SimInf_model object with result from simulation.

Arguments

model

The SimInf model to run.

...

Additional arguments.

solver

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

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. tools:::Rd_expr_doi("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. tools:::Rd_expr_doi("10.1007/978-3-319-10705-9_51")

Examples

## For reproducibility, call the set.seed() function and specify
## the number of threads to use. To use all available threads,
## remove the set_num_threads() call.
set.seed(123)
set_num_threads(1)

## 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)