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4 - Variable pool size and perfect test

This method estimates prevalence and confidence limits for variable pool sizes and assumed 100% test sensitivity and specificity. This method should be used if you can be confident that the sensitivity and specificity of the test are both close to 100%, and if pool sizes vary.

For this analysis, it was assumed that samples from 210 individual fruit bats were aggregated into 40 pools of 5 samples each with 10 samples tested individually, that 20 pools and 2 individual samples produced a positive test result and that the test sensitivity and specificity were both 100%. Input values and results for this analysis are summarised in the table below.

Input values:
Confidence level	0.95
Pool size	Number of pools tested	Number of pools positive
5	40	20
1	10	2
Results:
Estimated Prevalence		0.1339
2.5 percentile		0.0871
97.5 percentile		0.1930

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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