Chapter 24 Probability of Disease Freedom Using Clustering Model

IN WAREHOUSE DEVELOPMENT

24.1 What is the Probability Of Disease Freedom Using Clustering Model?

The Probability Of Disease Freedom Using Clustering Model can be used after sampling to determine the probability that a sub-administrative area is disease-free given sample effort and considering clustering of hosts.

24.2 What Questions Does it Answer?

Question 1. What is the probability that each sub-administrative area is disease-free given the number of negative cases I collected over the past sampling season.? The model considers clustering behavior of hosts.

Note: If you wish to see the maximum possible underlying prevalence given your sampling effort use the Prevalence Estimator Data Export if your sample data contains information on age, sex, and source, and use Simple Undetected Prevalence Estimator if your sample data contains information on age, sex, and source.

Note: If you wish to see the probability of disease freedom using several years of historical sampling without finding a positive case, use Mode 1 of the Sample Allocation Model (SAM). SAM does not consider clustering of hosts.

24.3 Output Details

  • A map containing the probability that each sub-administrative area is disease-free considering host clustering.

24.4 Abbreviated Tutorial

  1. Run the model in the CWD Data Warehouse.
  2. View the map to see the probability of disease-freedom.
  3. Explore the model logs, input file, and output files used in the run.
  4. If the model did not run, check the model logs to understand required data that was missing.

24.5 Parameters Needed to Execute the Model

  • Model type: Select ‘Probability Of Disease Freedom Using Clustering Model’ from the drop-down list.

  • Season-year: Select one season-year for which to determine sample quotas.

  • Host density: The number of hosts that reside in one square kilometer of land area. OR

  • Population Size: The number of hosts that reside in the sub administrative unit.

  • Average cluster size: Average cluster size of hosts in the population in each sub administrative area. An integer value between 1 (1 host per cluster in the population) and the total population size (1 cluster in the entire population). Note: The software will automatically ensure that your cluster size does not exceed the population size.

  • Correlation in disease status: Correlation in disease status between hosts sharing a cluster. A decimal between 0 (disease status among hosts in a cluster is independent) and 0.995 (disease status is nearly perfectly correlated among hosts sharing a cluster).

  • Sensitivity of the diagnostic test: The performance of the diagnostic test in declaring a true positive. A decimal between 0 (not sensitive: test will not appropriately declare a true positive) and 0.999 (nearly perfect sensitivity: test has high performance in declaring a true positive).

24.6 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. Journal of Agricultural, Biological, and Environmental Sciences, 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. Management Agencies Can Leverage Animal Social Structure for Wildlife Disease Surveillance. Journal of Wildlife Diseases. Journal of Wildlife Diseases. https://doi.org/10.7589/JWD-D-24-00079.