rsu.sep.rs {epiR} | R Documentation |
Calculates the surveillance system (population-level) sensitivity for detection of disease assuming representative sampling, imperfect test sensitivity and perfect test specificity using the hypergeometric method if N
is known and the binomial method if N
is unknown.
rsu.sep.rs(N = NA, n, pstar, se.u = 1)
N |
scalar integer or vector of integers the same length as |
n |
scalar integer or vector of integers representing the number of units tested. |
pstar |
scalar numeric or vector of numbers the same length as |
se.u |
scalar numeric or 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: ## Three hundred samples are to be tested from a population of animals to ## confirm the absence of a disease. The total size of the population is ## unknown. Assuming a design prevalence of 0.01 and a test with ## diagnostic sensitivity of 0.95 will be used what is the sensitivity of ## disease detection at the population level? rsu.sep.rs(N = NA, n = 300, pstar = 0.01, se.u = 0.95) ## The sensitivity of disease detection at the population level is 0.943. ## EXAMPLE 2: ## Thirty animals from five herds ranging in size from 80 to 100 head are to be ## sampled to confirm the absence of a disease. Assuming a design prevalence ## of 0.01 and a test with diagnostic sensitivity of 0.95 will be used, what ## is the sensitivity of disease detection for each herd? N <- seq(from = 80, to = 100, by = 5) n <- rep(30, times = length(N)) rsu.sep.rs(N = N, n = n, pstar = 0.01, se.u = 0.95) ## The sensitivity of disease detection for each herd ranges from 0.28 to ## 0.36.