sirs_nsimul: Stochastic SIRS model with equal birth and deaths for...

In NicolasPhysentzides/popdyn: Population Dynamics

Description

A stochastic SIRS model with 3 compartments solved using Gillespie’s direct method.

Usage

sirs_nsimul(x, params, maxstep = 1000, nsims = 5)

Arguments

x	vector of 4 parameters time, S, I and R. time is the initial starting time, S is the Susceptibles, I is the Infecteds and R is the Recovereds.
params	vector of 4 parameters beta, gamma, mu and xi. beta is the transmission rate, gamma is the recovery rate, mu is the per capita death rate, and the population level birth rate adn xi is the is the rate which recovered individuals return to the susceptible statue
maxstep	numeric value that indicates the maximum number of transiction between the population. Defaults to 1000.
nsims	numeric value that indicates the number of simulations. Defaults to 5.

Details

S,I,R must be positive and S + I + R = N. N is the whole population. Every simulation stops when there are no Infecteds or when the maximum number of steps is reached.

Value

data.frameThe data.frame contains 5 columns. Second, third,fourth and fifth are the arguments of the input vector x. The first column indicates the specific number of the simulation. The column takes (.n) values from 1 up to nsims increasing by 1.

Examples

library('plyr')
set.seed(0)

nsims <- 8
xstart <- c(time=0,S=595,I=5,R=0) #initial conditions
params <- c(mu=0.015,beta=20,gamma=13, xi=5) #parameters

res <- sirs_nsimul(x = xstart, params = params, nsims = nsims)

par(mfrow=c(3,1), mar=c(2,4,1,1))

plot(S~time,data=res,type='n')
d_ply(res,".n",function(x)lines(S~time,data=x,col=.n))

plot(I~time,data=res,type='n')
d_ply(res,".n",function(x)lines(I~time,data=x,col=.n))

plot(R~time,data=res,type='n')
d_ply(res,".n",function(x)lines(R~time,data=x,col=.n))

NicolasPhysentzides/popdyn documentation built on Sept. 11, 2020, 2:26 p.m.