

Analyse the results of 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;
 Sample size tested;
 Number tested positive;
 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;
 Type I (1  herdsensitivity) and Type II (1  herdspecificity) error values for determining whether to accept/reject
the null or alternative hypothesis;
 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; and
 The desired precision of results (number of digits to be displayed after the decimal point).
The results are displayed in terms of the null and alternate hypothesis:
 The probability of the null hypothesis is the probability of observing this many reactors or fewer, if the
population was diseased at a level equal to or greater than the specified design prevalence.
If this probability is small, we can conclude that t is very unlikely that the popultion is
diseased. If the probability is large, then there is not enough evidence to conclude that the
population is free from disease.
 The probability of the alternative hypothesis is the probability of observing
this many reactors or more if the poplation was truly disease free. If this is small, then it is
very unlikely that the population is free from disease. If it is large, then it is connsistent
with there being no disease in the population.
 If both null and alternative probabilities
are small, it suggests that the population is not free from disease, but the prevalence is less
than the design prevalence specified.
 If both null and alternative probabilities are large, The sample size
was too small to distinguish a population with the specified design prevalence
from a diseasefree population.
