SIR.detsim: deterministic trajectory of SIR

In bbolker/fitsir: SIR fitting tools

Description

deterministic trajectory of SIR

Usage

SIR.detsim(t, params, type = c("prevalence", "incidence", "death"),
  grad = FALSE)

Arguments

t	time vector
params	parameter vector (beta, gamma, N, i0)
type	type of count data
grad	(logical) return gradient with respect to unconstrained parameters

Examples

pars <- c(beta=0.4,gamma=0.2,N=5000,i0=0.001)
times <- 0:50
ss.p <- SIR.detsim(times,pars)
ss.i <- SIR.detsim(times,pars,type="incidence")
ss.d <- SIR.detsim(times,pars,type="death")
matplot(data.frame(ss.p,ss.i,ss.d),type = "l",xlab="time",ylab="count")
legend(x=0,y=800,col=1:3,lty=1:3,legend=c("prevalence","incidence","death"))
all.equal(cumsum(c(5, ss.i[-length(ss.i)])) - cumsum(c(0, ss.d[-length(ss.d)])), ss.p)

bbolker/fitsir documentation built on June 4, 2019, 8:28 a.m.