rsu.sep.rsvarse {epiR}R Documentation

Surveillance system sensitivity assuming representative sampling and varying unit sensitivity


Calculates the surveillance system (population-level) sensitivity for detection of disease assuming representative sampling and varying unit sensitivity.


rsu.sep.rsvarse(N = NA, pstar, se.u)



scalar integer or vector of integers the same length as se.u, representing the population size. Use NA if unknown.


scalar representing the design prevalence.


vector of numbers the same length as N representing the individual unit sensitivities.


A vector of surveillance system (population-level) sensitivity estimates.


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.


## 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:
ntest <- round(runif(n = 50, min = 10, max = 30), digits = 0)

## 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: <- = NA, n = ntest, pstar = 0.05, se.u = 0.60)

## 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 =

## The surveillance system sensitivity is 0.20.

[Package epiR version 2.0.38 Index]