Two methods are provided for fixed pool sizes and tests with known sensitivity and specificity, one producing asymptotic confidence intervals (Method 3) and the other exact confidence intervals (Method 4). These methods assume that the true values of both sensitivity and specificity are known exactly (i.e. that there is no uncertainty about the values). They do not allow for additional uncertainty in the prevalence estimate associated with uncertainty about test performance. They should be used if the test is well characterised and you can be confident that the sensitivity and specificity of the test are both close to the estimated values. If the true prevalence is likely to be close to zero, Method 4 (with exact confidence limits) should be used in preference to Method 3, because Method 3 could produce a negative lower confidence limit. Asymptotic confidence intervals (Method 3) can also be substantially narrower than exact intervals (Method 4) in some instances.

For this analysis, it was assumed that samples from 100 individual fruit bats were aggregated into 20 pools of 5 samples each, that 10 pools produced a positive test result and that the test sensitivity was 90% and specificity was 100%. An assumed sensitivity of less than 100% was used to demonstrate the possible effect of dilution on sensitivity of the pooled test. Input values and results for this analysis are summarised in the table below.

Method 3 | Method 4 | |
---|---|---|

Input values: | ||

Number of pools tested | 20 | 20 |

Number of pools positive | 10 | 10 |

Pool size | 5 | 5 |

Sensitivity | 0.9 | 0.9 |

Specificity | 1.0 | 1.0 |

Lower CL | 0.025 | 0.025 |

Upper CL | 0.975 | 0.975 |

Results: | ||

Estimated Prevalence | 0.1497 | 0.1497 |

2.5 percentile | 0.0943 | 0.0694 |

97.5 percentile | 0.2052 | 0.2818 |

Standard Error | 0.0283 | 0.0283 |

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