rsu.sep.rspool {epiR} R Documentation

## Surveillance system sensitivity assuming representative sampling, imperfect pooled sensitivity and perfect pooled specificity

### Description

Calculates the surveillance system (population-level) sensitivity and specificity for detection of disease assuming representative sampling and allowing for imperfect sensitivity and specificity of the pooled test.

### Usage

```rsu.sep.rspool(r, k, pstar, pse, psp = 1)
```

### Arguments

 `r` scalar or vector representing the number of pools. `k` scalar or vector of the same length as `r` representing the number of individual units that contribute to each pool (i.e. the pool size). `pstar` scalar or vector of the same length as `r` representing the design prevalence. `pse` scalar or vector of the same length as `r` representing the pool-level sensitivity. `psp` scalar or vector of the same length as `r` representing the pool-level specificity.

### Value

A list comprised of two elements:

 `se.p` scalar or vector, the surveillance system (population-level) sensitivity estimates. `sp.p` scalar or vector, the surveillance system (population-level) specificity estimates.

### References

Christensen J, Gardner I (2000). Herd-level interpretation of test results for epidemiologic studies of animal diseases. Preventive Veterinary Medicine 45: 83 - 106.

### Examples

```## EXAMPLE 1:
## To confirm your country's disease freedom status you intend to use a test
## applied at the herd level. The test is expensive so you decide to pool the
## samples taken from individual herds. If you decide to collect 60 pools,
## each comprised of samples from five herds what is the sensitivity of
## disease detection assuming a design prevalence of 0.01 and the sensitivity
## and specificity of the pooled test equals 1.0?

rsu.sep.rspool(r = 60, k = 5, pstar = 0.01, pse = 1, psp = 1)

## This testing regime returns a population-level sensitivity of disease
## detection of 0.95.

## EXAMPLE 2:
## Repeat these calculations assuming the sensitivity of the pooled test
## equals 0.90.

rsu.sep.rspool(r = 60, k = 5, pstar = 0.01, pse = 0.90, psp = 1)

## If the sensitivity of the pooled test equals 0.90 the population-level
## sensitivity of disease detection is 0.93. How can we improve population-
## level sensitivity? Answer: include more pools in the study.

rsu.sep.rspool(r = 70, k = 5, pstar = 0.01, pse = 0.90, psp = 1)

## Testing 70 pools, each comprised of samples from 5 herds returns a
## population-level sensitivity of disease detection of 0.95.

```

[Package epiR version 2.0.38 Index]