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8 - Sample size calculation for fixed pool size and perfect tests

This option can be used to calculate the number of pools that need to be tested for various values for pool size, estimated prevalence and desired confidence and precision of the estimate. It assumes fixed pool sizes and a test with 100% sensitivity and specificity.

For this analysis, sample size was calculated to provide 95% confidence of estimating a true prevalence of 0.14 (14%) with a precision of 0.055 (5.5%). This is equivalent to the observed prevalence and precision when 162 samples from little red flying foxes in Queensland were tested individually, with 22 positive results (H. Field, pers com). Input values and summary results are shown below. For a pool size of five, 42 pools would be sufficient to provide the desired precision and confidence in the estimated prevalence.

Input	Value
Prevalence	0.14
Precision	0.055
Confidence	0.95

Pool size	Number of pools	Total number of individual samples
1	153	153
2	83	166
5	42	210
10	33	330
15	36	540
20	46	920
25	64	1600
30	95	2850

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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