The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
The Kermack-McKendrick SIR model (Brown, Rothery, et al. 1993) is defined as
dS/dt = -beta*N*S
dI/dt = beta*N*S - gamma*I
dR/dt = gamma*I
Note that simulations of this model can generate in all zero propensity, if the first reaction is a recovery of the single ‘Infected’ individual.
Define parameters
library(GillespieSSA2)
<- "Kermack-McKendrick SIR model"
sim_name <- c(beta = .001, gamma = .1)
params <- 100
final_time <- c(S = 500, I = 1, R = 0) initial_state
Define reactions
<- list(
reactions reaction("beta * S * I", c(S = -1, I = +1), name = "transmission"),
reaction("gamma * I", c(I = -1, R = +1), name = "recovery")
)
Run simulations with the Exact method
set.seed(1)
<- ssa(
out initial_state = initial_state,
reactions = reactions,
params = params,
final_time = final_time,
method = ssa_exact(),
sim_name = sim_name
) plot_ssa(out)
Run simulations with the Explict tau-leap method
set.seed(1)
<- ssa(
out initial_state = initial_state,
reactions = reactions,
params = params,
final_time = final_time,
method = ssa_etl(),
sim_name = sim_name
) plot_ssa(out)
Run simulations with the Binomial tau-leap method
set.seed(2)
<- ssa(
out initial_state = initial_state,
reactions = reactions,
params = params,
final_time = final_time,
method = ssa_btl(),
sim_name = sim_name
) plot_ssa(out)
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.