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Helman SK, Mummah RO, Gostic KM, et al. Estimating prevalence and test accuracy in disease ecology: How Bayesian latent class analysis can boost or bias imperfect test results. Ecol Evol. 2020;10(14):7221-7232doi: 10.1002/ece3.6448.
Helman, S. K., Mummah, R. O., Gostic, K. M., Buhnerkempe, M. G., Prager, K. C., & Lloyd-Smith, J. O. (2020). Estimating prevalence and test accuracy in disease ecology: How Bayesian latent class analysis can boost or bias imperfect test results. Ecology and evolution, 10(14), 7221-7232. https://doi.org/10.1002/ece3.6448
Helman, Sarah K, et al. "Estimating prevalence and test accuracy in disease ecology: How Bayesian latent class analysis can boost or bias imperfect test results." Ecology and evolution vol. 10,14 (2020): 7221-7232. doi: https://doi.org/10.1002/ece3.6448
Helman SK, Mummah RO, Gostic KM, Buhnerkempe MG, Prager KC, Lloyd-Smith JO. Estimating prevalence and test accuracy in disease ecology: How Bayesian latent class analysis can boost or bias imperfect test results. Ecol Evol. 2020 Jun 15;10(14):7221-7232. doi: 10.1002/ece3.6448. eCollection 2020 Jul. PMID: 32760523; PMCID: PMC7391344.
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Ecol Evol. 2020 Jun 15;10(14):7221-7232. doi: 10.1002/ece3.6448. eCollection 2020 Jul.

Estimating prevalence and test accuracy in disease ecology: How Bayesian latent class analysis can boost or bias imperfect test results.

Ecology and evolution

Sarah K Helman, Riley O Mummah, Katelyn M Gostic, Michael G Buhnerkempe, Katherine C Prager, James O Lloyd-Smith

Affiliations

  1. Department of Ecology and Evolutionary Biology University of California, Los Angeles Los Angeles CA USA.
  2. Department of Internal Medicine Southern Illinois University School of Medicine Springfield IL USA.
  3. Fogarty International Center National Institutes of Health Bethesda MD USA.

PMID: 32760523 PMCID: PMC7391344 DOI: 10.1002/ece3.6448

Abstract

Obtaining accurate estimates of disease prevalence is crucial for the monitoring and management of wildlife populations but can be difficult if different diagnostic tests yield conflicting results and if the accuracy of each diagnostic test is unknown. Bayesian latent class analysis (BLCA) modeling offers a potential solution, providing estimates of prevalence levels and diagnostic test accuracy under the realistic assumption that no diagnostic test is perfect.In typical applications of this approach, the specificity of one test is fixed at or close to 100%, allowing the model to simultaneously estimate the sensitivity and specificity of all other tests, in addition to infection prevalence. In wildlife systems, a test with near-perfect specificity is not always available, so we simulated data to investigate how decreasing this fixed specificity value affects the accuracy of model estimates.We used simulations to explore how the trade-off between diagnostic test specificity and sensitivity impacts prevalence estimates and found that directional biases depend on pathogen prevalence. Both the precision and accuracy of results depend on the sample size, the diagnostic tests used, and the true infection prevalence, so these factors should be considered when applying BLCA to estimate disease prevalence and diagnostic test accuracy in wildlife systems. A wildlife disease case study, focusing on leptospirosis in California sea lions, demonstrated the potential for Bayesian latent class methods to provide reliable estimates under real-world conditions.We delineate conditions under which BLCA improves upon the results from a single diagnostic across a range of prevalence levels and sample sizes, demonstrating when this method is preferable for disease ecologists working in a wide variety of pathogen systems.

© 2020 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

Keywords: Bayesian latent class; California sea lion; diagnostic test; disease; infection; prevalence; sensitivity; specificity

Conflict of interest statement

The authors declare no competing interests.

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