Calculate least-cost sample sizes for 2-stage surveys for demonstrating disease freedom.
This analysis calculates the number of clusters and the number of units within each cluster
to be tested to provide a specified system sensitivity (probability of detecting disease)
for the given unit and cluster-level design prevalences and test sensitivity. Calculations
are based on actual cluster sizes provided (for the entire population) and a list of randomly selected clusters, along with
the number of units to sample for each selected cluster is included in the outputs. Test
specificity is assumed to be 100% (or follow-up testing of any positive will be undertaken
to confirm or exclude disease).
Sample sizes are optimised to minimise overall cost for given cluster and unit-level
testing costs. A maximum sample size per cluster can be specified, if desired and calculations
can be specified to ensure either a fixed sample size per cluster or a fixed (minimum) cluster sensitivity.
Sample sizes are calculated using the hypergeometric
probability approximation (assuming sampling without replacement).
Design prevalence (specified level of disease to be detected) must be specified
at both unit and cluster levels. Design prevalence can be specified as either:
Inputs required include:
Outputs from the analysis include:
If it is not possible to achieve the desired system sensitivity by testing all
(or the specified maximum number of) units in all of the herds, a message will be
returned, along with a summary of the achieved mean SeH and SSe if all units were
tested. In this case a list of all clusters, and the SeH achieved if all or the maximum
number of units were tested.
Paste herd testing data in the space below. Data columns can be
in any order but must include columns for herd size (labeled "HerdSize")
and herd id (labeled "HerdID"). A header row specifying column
names must also be included.
Download example data
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