epiR (version 2.0.78)

rsu.sep.rbvarse: Surveillance system sensitivity assuming risk based sampling and varying unit sensitivity

Description

Calculates the surveillance system (population-level) sensitivity for detection of disease assuming risk based sampling and varying unit sensitivity.

Usage

rsu.sep.rbvarse(N, rr, df, pstar)

Value

A list comprised of five elements:

sep

scalar, the population-level sensitivity estimate.

epi

vector, effective probability of infection estimates.

adj.risk

vector, adjusted risks.

n

vector, sample size by risk group

se.u

a vector of the mean sensitivity for each risk group.

Arguments

N

scalar integer or vector of integers the same length as rr, representing the population size. Use NA if unknown.

rr

relative risk values (vector of values corresponding to the number of risk strata).

df

dataframe of values for each combination of risk stratum and sensitivity level, column 1 = risk group index, column 2 = unit sensitivity, column 3 = n (sample size for risk group and unit sensitivity).

pstar

scalar representing the design prevalence.

References

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.

Examples

## EXAMPLE 1:
## A study has been carried out to detect Johne's disease in a population of 
## cattle. There are two risk groups ('high' and 'low') with the risk of 
## disease in the high risk group five times that of the low risk group.
## The number of  animals sampled and unit sensitivity varies by risk group, as
## detailed below. Assume there number of cattle in the high risk and low risk 
## group is 200 and 1800, respectively.

## Calculate the surveillance system sensitivity assuming a design prevalence
## of 0.01.

rg <- c(1,1,2,2)
se.u <- c(0.92,0.85,0.92,0.85)
n <- c(80,30,20,30)
df <- data.frame(rg = rg, se.u = se.u, n = n)

rsu.sep.rbvarse(N = c(200,1800), rr = c(5,1), df = df, pstar = 0.01)

## The surveillance system sensitivity is 0.99.