run {SimInf} | R Documentation |
Run the SimInf stochastic simulation algorithm
run(model, threads = NULL, solver = c("ssm", "aem")) ## S4 method for signature 'SimInf_model' run(model, threads = NULL, solver = c("ssm", "aem"))
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'. |
SimInf_model
object with result from simulation.
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
## 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)