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Pooled Prevalence Calculator - Demonstration analyses

Background

For these demonstration analyses (except where otherwise stated), a hypothetical pooled testing strategy for the estimation of prevalence of Hendra virus in fruit bats was used. The data was based on real data for Hendra virus testing in the little red flying fox (Pteropus scapulatus) in Queensland during the period 1996 - 1999 (H. Field, pers com). During this period, 162 samples were tested from little red flying foxes, with 22 samples positive, for an estimated prevalence of 13.6% (95% CI: 8.7 - 19.8%). For these analyses, pool sizes and numbers of pools were calculated to provide 95% confidence of estimating a true prevalence of 14% with a desired precision of ± 5.5% for the various estimation methods, corresponding to the estimated prevalence and confidence interval for the original (unpooled) data. The most frequent result from simulation studies was then used to estimate the prevalence and confidence interval for each scenario.

Contents


Contents
1 Fixed pool size and perfect tests
2 Fixed pool size and tests with known sensitivity and specificity
3 Fixed pool size and tests with uncertain sensitivity and specificity
4 Variable pool size and perfect test
5 Pooled prevalence using a Gibbs sampler
6 Estimated true prevalence using one test (unpooled) with a Gibbs sampler
7 Estimated true prevalence using two tests (unpooled) with a Gibbs sampler
8 Sample size calculation for fixed pool size and perfect tests
9 Sample size calculation for fixed pool size and tests with known sensitivity and specificity
10 Sample size calculation for fixed pool size and tests with uncertain sensitivity and specificity
11 Simulate sampling for fixed pool size and assumed perfect test
12 Simulate sampling for fixed pool size and test with known sensitivity and specificity
13 Simulate sampling for fixed pool size and test with uncertain sensitivity and specificity
14 Simulate sampling for variable pool size and assumed perfect test
15 Demonstration of freedom using pooled testing with tests of known sensitivity and fixed pool size
16 Estimation of alpha and beta Parameters for Prior Beta distributions
17 Estimation of Beta probability distributions for specified alpha and beta parameters