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.

SIRS metapopulation model (Pineda-Krch, 2008)

The SIRS epidemiological metapopulation model is defined in Pineda-Krch (2008).

Load package

library(GillespieSSA)

Define parameters

patchPopSize <- 500                    # Patch size
U <- 20                                # Number of patches
simName <- "SIRS metapopulation model" # Simulation name
tf <- 50                               # Final time

parms <- c(
  beta = 0.001,                        # Transmission rate
  gamma = 0.1,                         # Recovery rate
  rho = 0.005,                         # Loss of immunity rate
  epsilon = 0.01,                      # Proportion inter-patch transmissions
  N = patchPopSize                     # Patch population size (constant)
)

Create the named initial state vector for the U-patch system. The structure of x0 is as follows (assuming a patchsize of 500 individuals),

x0 <- c(
  S1 = 499, I1 = 1, 
  S2 = 500, I2 = 0,
  S3 = 500, I3 = 0, 
  ... 
  S20 = 500, I20 = 0
)
x0 <- c(patchPopSize - 1, 1, rep(c(patchPopSize, 0), U - 1))
names(x0) <- unlist(lapply(seq_len(U), function(i) paste0(c("S", "I"), i)))

Define the state change matrix for a single patch

nu <- matrix(c(-1, -1,  0, +1,  # S
               +1, +1, -1,  0), # I
             nrow=2,byrow=TRUE)

Define propensity functions

a <- unlist(lapply(
  seq_len(U),
  function(patch) {
    i <- patch
    j <- if (patch == 1) U else patch - 1
    
    c(
      paste0("(1-epsilon)*beta*S", i, "*I", i), # Intra-patch infection
      paste0("epsilon*beta*S", i, "*I", j),     # Inter-patch infection
      paste0("gamma*I", i),                     # Recovery from infection
      paste0("rho*(N-S", i, "-I", i, ")")       # Loss of immunity
    )
  }
))

Run simulations with the Direct method

set.seed(1)
out <- ssa(
  x0 = x0,
  a = a,
  nu = nu,
  parms = parms,
  tf = tf,
  method = ssa.d(),
  simName = simName,
  verbose = FALSE,
  consoleInterval = 1
) 
ssa.plot(out, by = 5, show.title = TRUE, show.legend = FALSE)

Run simulations with the Explict tau-leap method

set.seed(1)
out <- ssa(
  x0 = x0,
  a = a,
  nu = nu,
  parms = parms,
  tf = tf,
  method = ssa.etl(),
  simName = simName,
  verbose = FALSE,
  consoleInterval = 1
) 
ssa.plot(out, by = 5, show.title = TRUE, show.legend = FALSE)

Run simulations with the Binomial tau-leap method

set.seed(1)
out <- ssa(
  x0 = x0,
  a = a,
  nu = nu,
  parms = parms,
  tf = tf,
  method = ssa.btl(),
  simName = simName,
  verbose = FALSE,
  consoleInterval = 1
) 
ssa.plot(out, by = 5, show.title = TRUE, show.legend = FALSE)

Run simulations with the Optimized tau-leap method

set.seed(1)
out <- ssa(
  x0 = x0,
  a = a,
  nu = nu,
  parms = parms,
  tf = tf,
  method = ssa.otl(hor = rep(2, length(x0))),
  simName = simName,
  verbose = FALSE,
  consoleInterval = 1
) 
ssa.plot(out, by = 5, show.title = TRUE, show.legend = FALSE)

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.