Population level (or herd/flock) sensitivity using pooled sampling

This utility calculates a population or cluster level sensitivity for a survey where pooled sammpling has been used. This is the probability (or level of confidence) that one or more pools will give a positive result assuming the disease was present at a prevalence greater than or equal to the specified design prevalence. It is assumed that individual units (animals) are sampled and that samples are combined at random into a specified number of pools of equal size. Test sensitivity and specificity are measured at the pool level, allowing for any dilution effect.

For more details see: Christensen & Gardner (2000). Herd-level interpretation of test results for epidemiologic studies of animal diseases. Prev Vet Med. 45:83-106.

Inputs are:

• Design prevalence as a proportion;
• Pool sensitivity;
• Pool specificity;
• Pool size - the number of units represented in each pooled sample; and
• The number of pools tested.

Outputs are:

• Population-level sensitivity for the given pool size, number of pools, design prevalence and pool sensitivity and specificity.

No results

				################################################################
# program to calculate population-level sensitivity using pooled sampling
################################################################
# uses RSurveillance package

rm(list = ls())
test<- ifelse(length(commandArgs()) < 3, TRUE, FALSE)
fpath<- ifelse(test, "webRootUrl", "rtoolsPath")

# load header scripts
  source(paste(fpath, "R/epi_head.R", sep = ""))
  source(paste(fpath, "R/HTMLStream.R", sep = ""))
  source(paste(fpath, "R/epitools_functions.r", sep = ""))


# extract command arguments
# pstar, sens, r, k 
    a1<- type.convert(a0[8:12])


# initialise variables
pstar<- a1[1]
Sens<- a1[2]
sp<- a1[3]
r<- a1[4]
k<- a1[5]
digits<- 4
filename<- digest(Sys.time)
graphfile<- paste(fpath, "tmp/", filename, ".png", sep="")
sinkfile<- paste(fpath, "tmp/", filename, ".txt", sep="")

# table of inputs
inputs<- array("", dim = c(length(a1), 1))
inputs[, 1]<- a1
rownames(inputs)<- c("Design prevalence (Pstar)", "Pool-level sensitivity","Pool-level specificity","Number of pools", "Pool size")



result<- sep.pooled(r, k, pstar, Sens, sp)
seh<- round(result[[1]], digits)
results1<- rbind(inputs, "Population sensitivity" = seh, "Population specificity" = round(result[[2]], digits))


# write to html and file
heading<- "Population or Cluster level sensitivity with pooled testing"
subheadings<- ""
tmp.file<- paste(fpath, "tmp/", filename, sep = "")
output<- html.output(heading, subheadings, "", results = list(results1), graphs = "", graph.headings = "", show.inputs = F, show.graphs = F, tmp.file, result.txt = "") 
write.html(output, tmp.file)
cat(output)