rsu.pfree.equ(se.p, p.intro)

## EXAMPLE 1:
## The current (ongoing) surveillance system for a given disease in your 
## country has been estimated to have a population sensitivity of 0.60 per 
## time period (one year). Assuming the probability of disease introduction 
## per unit time is 0.02, what is the eventual plateau level for confidence 
## of freedom and how long will it take to reach this level, assuming a 
# prior (starting) confidence of freedom of 0.50?

## Firstly, estimate the equilibrium (plateau) confidence of freedom:

conf.eq <- rsu.pfree.equ(se.p = 0.60, p.intro = 0.02)
conf.eq

## The equilibrium discounted probability of disease freedom is 0.986.

## Next, calculate confidence of freedom over 20 time periods for se.p = 0.60
## and p.intro = 0.02:

rval.df <- rsu.pfree.rs (se.p = rep(0.6, times = 20), 
   p.intro = rep(0.02, times = 20), prior = 0.50)
head(rval.df)

## When does the confidence of freedom first reach the equilibrium value 
## (rounded to 3 digits)? 

rsep.p <- which(rval.df$pfree >= round(conf.eq$depfree, digits = 3))
rsep.p[1]

## It takes 9 time periods (years) to reach the equilibrium level of 0.986. 


## EXAMPLE 2:
## You have been asked to design a surveillance system to detect a given 
## disease in your country. If the probability of disease introduction per 
## unit time is 0.10, what surveillance system sensitivity do you need to 
## be 95% certain that disease is absent based on the testing carried out as 
## part of your program?

## Generate a vector of candidate surveillance system sensitivity estimates 
## from 0.1 to 0.99:

se.p <- seq(from = 0.10, to = 0.99, by = 0.01)

## Calculate the probability of disease freedom for each of the candidate 
## surveillance system sensitivity estimates:

rval.df <- rsu.pfree.equ(se.p = se.p, p.intro = 0.10)
rval.df <- data.frame(se.p = se.p, depfree = rval.df$depfree)
head(rval.df)

## Which of the surveillance system sensitivity estimates returns a 
## probability of freedom greater than 0.95?

rsep.p <- rval.df$se.p[rval.df$depfree > 0.95]
rsep.p[1]

## The required surveillance system sensitivity for this program is 0.69. 
## Plot the results:

## Not run: 
library(ggplot2)

ggplot(data = rval.df, aes(x = se.p, y = depfree)) +
   geom_point() + 
   geom_line() + 
   scale_x_continuous(limits = c(0,1), 
      name = "Surveillance system sensitivity") + 
   scale_y_continuous(limits = c(0,1), 
      name = "Equilibrium discounted probability of disease freedom") +
  geom_hline(aes(yintercept = 0.95), linetype = "dashed") +
  geom_vline(aes(xintercept = rsep.p[1]), linetype = "dashed") +
   theme_bw()
   
## End(Not run)