rsu.sep.cens {epiR} | R Documentation |
Calculates the surveillance system (population-level) sensitivity for disease detection assuming imperfect test sensitivity, perfect test specificity and when every unit in the population is tested (a census).
rsu.sep.cens(d = 1, se.u)
d |
scalar integer defining the expected number of infected units in the population (that is, the population size multiplied by the design prevalence). |
se.u |
scalar or vector of numbers between 0 and 1 defining the unit sensitivity of the test. |
A vector of surveillance system (population-level) sensitivities.)
## EXAMPLE 1: ## Every animal in a population is to be sampled and tested using a test ## with a diagnostic sensitivity of 0.80. What is the probability that ## disease will be detected if we expect that there are five infected animals ## in the population? rsu.sep.cens(d = 5, se.u = 0.80) ## The probability that disease will be detected (i.e. the surveillance ## system sensitivity) is 0.99 (i.e. quite high, even though the sensitivity ## of the test is relatively low). ## EXAMPLE 2: ## Calculate the surveillance system sensitivity assuming every animal in ## populations of size 10, 50, 100, 250 and 500 will be sampled and tested, ## assuming a design prevalence in each population of 0.01 and use of a test ## with a diagnostic sensitivity of 0.92. rsu.sep.cens(d = ceiling(0.01 * c(10, 50, 100, 250, 500)), se.u = 0.92) ## For the populations comprised of 100 animals or less the surveillance ## system sensitivity is 0.92. For the populations comprised of greater than ## or equal to 250 animals the surveillance system sensitivity is greater ## than 0.99.