run: Run the SimInf stochastic simulation algorithm

In SimInf: A Framework for Data-Driven Stochastic Disease Spread Simulations

Run the SimInf stochastic simulation algorithm

Description

Run the SimInf stochastic simulation algorithm

Usage

run(model, ...)

## S4 method for signature 'SimInf_model'
run(model, solver = c("ssm", "aem"), ...)

## S4 method for signature 'SEIR'
run(model, solver = c("ssm", "aem"), ...)

## S4 method for signature 'SIR'
run(model, solver = c("ssm", "aem"), ...)

## S4 method for signature 'SIS'
run(model, solver = c("ssm", "aem"), ...)

## S4 method for signature 'SISe'
run(model, solver = c("ssm", "aem"), ...)

## S4 method for signature 'SISe3'
run(model, solver = c("ssm", "aem"), ...)

## S4 method for signature 'SISe3_sp'
run(model, solver = c("ssm", "aem"), ...)

## S4 method for signature 'SISe_sp'
run(model, solver = c("ssm", "aem"), ...)

## S4 method for signature '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

\Widgren

2019

\Bauer

2016

\Bauer

2015

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)

SimInf documentation built on June 22, 2024, 9:27 a.m.