rsu.sep {epiR} | R Documentation |

Calculates the probability that the prevalence of disease in a population is less than or equal to a specified design prevalence following return of a specified number of negative test results.

rsu.sep(N, n, pstar, se.u)

`N` |
scalar or vector, integer representing the population size. |

`n` |
scalar or vector, integer representing the number of units sampled. |

`pstar` |
scalar or vector of the same length as |

`se.u` |
scalar or vector of the same length as |

A vector of the estimated probability that the prevalence of disease in the population is less than or equal to the specified design prevalence.

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: ## The population size in a provincial area is 193,000. In a given two- ## week period 7764 individuals have been tested for COVID-19 using an ## approved PCR test which is believed to have a diagnostic sensitivity of ## 0.85. All individuals have returned a negative result. What is the ## probability that the prevalence of COVID-19 in this population is less ## than or equal to 100 cases per 100,000? rsu.sep(N = 193000, n = 7764, pstar = 100 / 100000, se.u = 0.85) ## If all of the 7764 individuals returned a negative test we can be more than ## 99% confident that the prevalence of COVID-19 in the province is less ## than 100 per 100,000. ## EXAMPLE 2: ## What is the probability that the prevalence of COVID-19 is less than or ## equal to 10 cases per 100,000? rsu.sep(N = 193000, n = 7764, pstar = 10 / 100000, se.u = 0.85) ## If all of the 7764 individuals returned a negative test we can be 49% ## confident that the prevalence of COVID-19 in the province is less ## than 10 per 100,000. ## EXAMPLE 3: ## In a population of 1000 individuals 474 have been tested for disease X ## using a test with diagnostic sensitivity of 0.95. If all individuals tested ## have returned a negative result what is the maximum prevalence expected ## if disease is actually present in the population (i.e. what is the design ## prevalence)? pstar <- rsu.pstar(N = 1000, n = 474, se.p = 0.95, se.u = 0.95) pstar ## If 474 individuals are tested from a population of 1000 and each returns a ## negative result we can be 95% confident that the maximum prevalence (if ## disease is actually present in the population) is 0.005. ## Confirm these calculations using function rsu.sep. If 474 individuals out ## of a population of 1000 are tested using a test with diagnostic sensitivity ## 0.95 and all return a negative result how confident can we be that the ## prevalence of disease in this population is 0.005 or less? rsu.sep(N = 1000, n = 474, pstar = pstar, se.u = 0.95) ## The surveillance system sensitivity is 0.95.

[Package *epiR* version 2.0.38 Index]