epiR: rsu.sep.rs – R documentation

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rsu.sep.rs

Surveillance system sensitivity assuming representative sampling

Description

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.

Usage

rsu.sep.rs(N = NA, n, pstar, se.u = 1)

Arguments

`N`	scalar integer or vector of integers the same length as `n`, representing the population size. Use `NA` if unknown.
`n`	scalar integer or vector of integers representing the number of units tested.
`pstar`	scalar numeric or vector of numbers the same length as `n` representing the design prevalence. See details, below.
`se.u`	scalar numeric or vector of numbers the same length as `n` representing the unit sensitivity.

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:
## 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.

epiR

Tools for the Analysis of Epidemiological Data

v2.0.19

GPL (>= 2)

Authors

Mark Stevenson <mark.stevenson1@unimelb.edu.au> and Evan Sergeant <evansergeant@gmail.com> with contributions from Telmo Nunes, Cord Heuer, Jonathon Marshall, Javier Sanchez, Ron Thornton, Jeno Reiczigel, Jim Robison-Cox, Paola Sebastiani, Peter Solymos, Kazuki Yoshida, Geoff Jones, Sarah Pirikahu, Simon Firestone, Ryan Kyle, Johann Popp, Mathew Jay and Charles Reynard.

Initial release

2021-01-12