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 48% ## confident that the prevalence of COVID-19 in the province is less ## than 10 per 100,000.