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Pooled Prevalence

Simulate sampling for fixed pool size and assumed perfect test

Design parameters:

Simulation details:

Paste data in the space below. Data columns can be in any order but must include columns labeled "Poolsize" and "Pools". A header row specifying column names must also be included.

Download example data


This utility simulates sampling and prevalence estimation for alternative pooling strategies for an assumed prevalence value and for a specified level of confidence. The program runs multiple iterations of sampling and estimation and calculates the mean prevalence and estimated bias across all iterations. See the User Guide for more details. See demonstration analysis.

This method estimates prevalence using a method that assumes that test sensitivity and specificity are both 100%. However, if the true sensivitity and specificity of the test are likely to be less than 100% estimates of the true values can be entered to investigate the potential impact of this assumption being invalid on the resulting prevalence estimate.

Input values

Required inputs for this program are:

  • assumed true prevalence of infection - between 0 and 1;
  • estimates of likely true test sensitivity and specificity - between 0 and 1;
  • the desired level of confidence - between 0 and 1;
  • the number of iterations to simulate - a positive integer; and
  • the size and number of pools to be tested for each strategy to be simulated. This data should be copied and pasted from a spreadsheet format as described below.


Outputs are summarised across all iterations for each strategy entered and presented in a summary table. The main outputs are:

  • mean prevalence;
  • minimum and maximum prevalence estimates;
  • mean bias in the estimated prevalence;
  • mean (exact) confidence interval width;
  • mean standard error of the estimated prevalence;
  • mean squared error of the estimated prevalence (mean variance plus the square of the mean bias);
  • relative bias as a proportion of the mean estimated (apparent) prevalence (AP);
  • relative bias as a proportion of the specified design (true) prevalence (TP);
  • squared mean bias as a proportion of the mean squared error; and
  • proportion of "valid" estimates, where the confidence interval for the estimated prevalence contains the true (design) prevalence.


Data for pool sizes and associated numbers of pools tested should be pasted into the data submission area. You can enter any number of scenarios to simulate, with a new row required for each scenario. You must enter at least two columns of data, labeled "PoolSize" and "Pools". Include a header row containing the names.