Prevalence is a common epidemiological measure for assessing soil-transmitted helminth
burden and forms the basis for much public-health decision-making. Standard diagnostic
techniques are based on egg detection in stool samples through microscopy and these techniques are known to have poor sensitivity for individuals with low infection intensity, leading
to poor sensitivity in low prevalence populations. PCR diagnostic techniques offer very high
sensitivities even at low prevalence, but at a greater cost for each diagnostic test in terms of
equipment needed and technician time and training. Pooling of samples can allow prevalence to be estimated while minimizing the number of tests performed. We develop a model
of the relative cost of pooling to estimate prevalence, compared to the direct approach of
testing all samples individually. Analysis shows how expected relative cost depends on both
the underlying prevalence in the population and the size of the pools constructed. A critical
prevalence level (approx. 31%) above which pooling is never cost effective, independent of
pool size. When no prevalence information is available, there is no basis on which to choose
between pooling and testing all samples individually. We recast our model of relative cost in
a Bayesian framework in order to investigate how prior information about prevalence in a
given population can be used to inform the decision to choose either pooling or full testing.
Results suggest that if prevalence is below 10%, a relatively small exploratory prevalence
survey (10–15 samples) can be sufficient to give a high degree of certainty that pooling may
be relatively cost effective.