# sep.binom: Binomial Population sensitivity In RSurveillance: Design and Analysis of Disease Surveillance Activities

## Description

Calculates population sensitivity for detecting disease, assuming imperfect test sensitivity and specificity and representative sampling, using binomial distribution (assumes large or unknown population size and that cut-point number of reactors for a positive result = 1)

## Usage

 `1` ```sep.binom(n, pstar, se = 1, sp = 1, dig = 5) ```

## Arguments

 `n` sample size = number of units tested (integer), scalar or vector `pstar` design prevalence as a proportion (scalar or vector of same length as n) `se` unit sensitivity of test (proportion), default = 1 (scalar or vector of same length as n) `sp` unit specificity of test (proportion), default = 1 (scalar or vector of same length as n) `dig` number of digits for rounding of results

## Value

vector of population-level sensitivities

## Examples

 ```1 2 3 4 5 6``` ```# examples for sep.binom - checked sep.binom(n=300, pstar = 0.02, se = 0.92) tested<- seq(10,100, by=10) prev<- 0.05 sens<- 0.9 sep.binom(tested, prev, sens) ```

RSurveillance documentation built on May 29, 2017, 11:52 p.m.