

Calculate the required sample size and cutpoint for testing to demonstrate
population freedom from disease
using imperfect tests an allowing for small populations.
This utility uses the methods described by: Cameron and Baldock (1998):
A new probability formula for surveys to substantiate freedom from disease.
Prev. Vet. Med. 34:117 and Cameron (1999): Survey Toolbox for Livestock
Diseases  A practical manual and software package for active surveillance of livestock
diseases in developing countries. Australian Centre for International Agricultural
Research, Canberra, Australia.
Inputs include:
 Size of the population sampled;
 Test sensitivity and specificity;
 Design prevalence (the hypothetical prevalence to be detected).
Design prevalence can be specified as either a fixed number of elements from the
population or a proportion of the population;
 Maximum acceptable Type I (1  populationsensitivity) and Type II (1  populationspecificity) error values for determining whether to accept/reject
the null or alternative hypothesis, assuming a null hypothesis that the popultion is diseased;
 Calculation method: hypergeometric (for small populations),
or simple binomial (for large populations);
 The population size threshold, above which the simple binomial method is used
regardless of which calculation method has been selected;
 The maximum upper limit for required sample size; and
 The desired precision of results (number of digits to be displayed after the decimal point).
The results are presented as:
 The minimum sample size and corresponding cutpoint number of positives to
achieve the specified type I and type II errors for the given population, design
prevalence and test performance;
 achieved Type I and Type II error levels and corresponding populationlevel sensitivities
and specificities;
 A descriptive interpretation of the results; and
 an error message if the desired error levels cannot be achieved within the limits of populatuon
and/or maximum sample size.
