Chapter 20 Sample Size Quotas Model

Last updated 24 December 2024

20.1 What is the Sample Size Quotas Model?

The Sample Size Quotas Model leverages naturally-occurring biological groupings and correlation structure within those groupings to plan a sampling scheme that provides information about some level of underlying disease prevalence in the greater population.

20.2 What Question Does it Answer?

Question 1. How many individuals should I test without finding a positive case to have high probability that disease prevalence is at or below some predetermined percentage?

20.3 Output Details

  • Sample size: The number of samples that must test negative for disease from each sub administrative unit to have high probability that the underlying disease prevalence is at or below the desired level.

20.4 Abbreviated Tutorial

  1. Choose your model parameters (see below).
  2. Run the model with those desired population parameters.
  3. Look at the map to see the number of animals that need to be tested without finding a positive case to ensure there is a high probability that disease prevalence is at or below your desired level.

20.5 Parameters Needed to Execute the Model

  • Model type: Select ‘Sample Size Quotas Model’ from the drop-down list.

  • Population size: Total number of hosts in the population in each sub administrative unit. An integer between 100 and 10,000,000.

  • Average cluster size: Average cluster size of hosts in the population in each sub administrative unit. An integer value between 1 (1 host per cluster in the population) and the Population size (1 cluster in the entire population).

  • Average correlation in disease status: Correlation in disease status between hosts in a cluster across the administrative area. A decimal between 0 (disease status among members of a cluster is independent) and 0.995 (disease status is nearly perfectly correlated among members sharing a cluster).

  • Sensitivity of the diagnostic test: Sensitivity of the diagnostic test used to diagnose the disease across the administrative unit. A decimal between 0 (not sensitive) and 0.999 (nearly perfect sensitivity).

  • Desired prevalence percentage: The maximum allowable underlying population-scale prevalence remaining in each sub administrative unit after statistically valid levels of sampling. An integer between 1 (population-scale prevalence is at or below 1%) and 99 (population scale prevalence is at or below 99%).

20.6 Details on the Theory

Booth JG, Hanley BJ, Hodel FH, Jennelle CS, Guinness J, Them CE, Mitchell CI, Ahmed MS, Schuler KL. 2024. Sample Size for Estimating Disease Prevalence in Free-Ranging Wildlife Populations: A Bayesian Modeling Approach. JABES 29, 438–454. https://doi.org/10.1007/s13253-023-00578-7.

Booth JG, Hanley BJ, Thompson NE, Gonzalez-Crespo C, Christensen SA, Jennelle CS, Caudell JN, Delisle Z, Guinness J, Hollingshead NA, Them CT, Schuler KL. Communicable disease among individuals in a homogenously mixing population dramatically reduces the cost of wildlife disease sampling. Journal of Wildlife Diseases. In press.

Hanley BJ, Booth JG, Hodel FH, Thompson NE, Bloodgood JCG, Dion JP, Van de Berg S, Gonzalez-Crespo C, Huang Y, Wang J, Miller LA, Hollingshead NA, Peaslee JL, Schuler KL. 2024. Sample size calculator for declaring a population free of infectious disease (Version 1) [Software]. Cornell University Library eCommons. https://doi.org/10.7298/ka5p-bj90