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\name{rsu.sep.rsvarse}
\alias{rsu.sep.rsvarse}
\title{
Surveillance system sensitivity assuming representative sampling and varying unit sensitivity
}
\description{
Calculates the surveillance system (population-level) sensitivity for detection of disease assuming representative sampling and varying unit sensitivity.
}
\usage{
rsu.sep.rsvarse(N = NA, pstar, se.u)
}
\arguments{
\item{N}{scalar integer or vector of integers the same length as \code{se.u}, representing the population size. Use \code{NA} if unknown.}
\item{pstar}{scalar representing the design prevalence.}
\item{se.u}{vector of numbers the same length as \code{N} representing the individual unit sensitivities.}
}
\value{
A vector of surveillance system (population-level) sensitivity estimates.
}
\references{
MacDiarmid S (1988). Future options for brucellosis surveillance in New Zealand beef herds. New Zealand Veterinary Journal 36: 39 - 42.
Martin S, Shoukri M, Thorburn M (1992). Evaluating the health status of herds based on tests applied to individuals. Preventive Veterinary Medicine 14: 33 - 43.
}
\examples{
## EXAMPLE 1:
## A study has been carried out to detect Johne's disease in a population of
## cattle. A random sample of 50 herds from a herd population of unknown size
## has been selected and, from each selected herd, a variable number of animals
## have been tested using faecal culture which is assumed to have a diagnostic
## sensitivity in the order of 0.60.
## The number of animals tested in each of the 50 herds is:
set.seed(1234)
ntest <- round(runif(n = 50, min = 10, max = 30), digits = 0)
ntest
## Calculate the herd level sensitivity of disease detection, assuming we've
## been provided with no details of the number of animals in each of the 50
## herds. Assume a within-herd design prevalence of 0.05:
herd.se <- rsu.sep.rs(N = NA, n = ntest, pstar = 0.05, se.u = 0.60)
range(herd.se)
## The herd level sensitivity of detection varies between 0.26 and 0.60.
## Calculate the surveillance system sensitivity assuming a herd-level design
## prevalence of 0.01:
rsu.sep.rsvarse(N = NA, pstar = 0.01, se.u = herd.se)
## The surveillance system sensitivity is 0.20.
}
\keyword{methods}
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