predicttsir {tsiR}

predicttsir

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)