rsu.sssep.rs2st {epiR} | R Documentation |

Calculates the required sample size to achieve a desired surveillance system sensitivity assuming two-stage sampling (sampling of clusters and sampling of units within clusters), imperfect test sensitivity and perfect test specificity.

rsu.sssep.rs2st(H = NA, N = NA, pstar.c, se.c, pstar.u, se.u, se.p)

`H` |
scalar, integer representing the total number of clusters in the population. Use |

`N` |
vector, integer representing the number of units within each cluster. Use |

`pstar.c` |
scalar, numeric (0 to 1) representing the cluster level design prevalence. |

`se.c` |
scalar, numeric (0 to 1) representing the required cluster level sensitivity. |

`pstar.u` |
scalar, numeric (0 to 1) representing the surveillance unit level design prevalence. |

`se.u` |
scalar (0 to 1) representing the sensitivity of the diagnostic test at the surveillance unit level. |

`se.p` |
scalar (0 to 1) representing the desired surveillance system (population-level) sensitivity. |

A list comprised of two data frames: `clusters`

and `units`

. Data frame `clusters`

lists:

`H` |
the total number of clusters in the population, as entered by the user. |

`nsample` |
the number of clusters to be sampled. |

Data frame `units`

lists:

`N` |
the number of units within each cluster, as entered by the user. |

`nsample` |
the number of units to be sampled. |

Cameron A, Baldock C (1998). A new probability formula for surveys to substantiate freedom from disease. Preventive Veterinary Medicine 34: 1 - 17.

Cameron A (1999). Survey Toolbox for Livestock Diseases — A practical manual and software package for active surveillance of livestock diseases in developing countries. Australian Centre for International Agricultural Research, Canberra, Australia.

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: ## Sampling is to be carried out to support a claim that a country is free ## of bovine brucellosis. We are not certain of the total number of herds ## in the country and we are not certain of the number of cows within each ## herd. ## The design prevalence for this study is set to 0.01 at the herd level and ## if a herd is positive for brucellosis the individual animal level ## design prevalence is set to 0.10. The sensitivity of the diagnostic ## test to be used is 0.95. ## How many herds and how many animals from within each herd ## need to be sampled to be 95% confident of detecting disease at the ## herd and individual animal level? rsu.sssep.rs2st(H = NA, N = NA, pstar.c = 0.01, se.c = 0.95, pstar.u = 0.10, se.u = 0.95, se.p = 0.95) ## A total of 314 herds need to be sampled, 31 cows from each herd. ## EXAMPLE 2: ## Now lets say we know that there are 500 cattle herds in the country and ## we have the results of a recent livestock census providing counts of the ## number of cattle in each herd. How many herds and how many animals from ## within each herd need to be sampled to be 95% confident of detecting ## disease at the herd and individual animal level? # Generate a vector of herd sizes. The minimum herd size is 25. set.seed(1234) hsize <- ceiling(rlnorm(n = 500, meanlog = 1.5, sdlog = 2)) + 25 nsample <- rsu.sssep.rs2st(H = 500, N = hsize, pstar.c = 0.01, se.c = 0.95, pstar.u = 0.10, se.u = 0.95, se.p = 0.95) nsample$clusters head(nsample$units) ## A total of 238 of the 500 herds need to be tested. The number of animals ## to sample from the first herd (comprised of 26 animals) is 18.

[Package *epiR* version 2.0.38 Index]