rsu.sep.rsvarse {epiR} | R Documentation |
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)
N |
scalar integer or vector of integers the same length as |
pstar |
scalar representing the design prevalence. |
se.u |
vector of numbers the same length as |
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.
## 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.