predicttsir: predicttsir

In tsiR: An Implementation of the TSIR Model

Description

function to predict incidence and susceptibles using the tsir model. This is different than simulatetsir as you are inputting parameters as vectors. The output is a data frame I and S with mean and confidence intervals of predictions.

Usage

predicttsir(times, births, beta, alpha, S0, I0, nsim, stochastic)

Arguments

times	The time vector to predict the model from. This assumes that the time step is equal to IP
births	The birth vector (of length length(times) or a single element) where each element is the births in that given (52/IP) time step
beta	The length(52/IP) beta vector of contact.
alpha	A single numeric which acts as the homogeniety parameter.
S0	The starting initial condition for S. This should be greater than one, i.e. not a fraction.
I0	The starting initial condition for I. This should be greater than one, i.e. not a fraction.
nsim	The number of simulations to perform.
stochastic	A TRUE / FALSE argument where FALSE is the deterministic model, and TRUE is a negative binomial distribution.

Examples

## Not run: 
require(kernlab)
require(ggplot2)
require(kernlab)
require(tsiR)
London <- twentymeas$London

London <- subset(London, time > 1950)

IP <- 2
## first estimate paramters from the London data
parms <- estpars(data=London, IP=2, regtype='gaussian')

plotbeta(parms)

## now lets predict forward 20 years using the mean birth rate,
## starting from rough initial conditions
births <- min(London$births)
times <- seq(1965,1985, by = 1/ (52/IP))
S0 <- parms$sbar
I0 <- 1e-5*mean(London$pop)

pred <- predicttsir(times=times,births=births,
                    beta=parms$contact$beta,alpha=parms$alpha,
                    S0=S0,I0=I0,
                    nsim=50,stochastic=T)

## plot this prediction
ggplot(pred$I,aes(time,mean))+geom_line()+geom_ribbon(aes(ymin=low,ymax=high),alpha=0.3)



## End(Not run)

tsiR documentation built on Jan. 21, 2021, 1:06 a.m.