# 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

 `1` ```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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33``` ```## 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.