Bayesian latent class modeling to evaluate the predictive value of feline leukemia virus and feline immunodeficiency virus testing in apparently healthy and clinically ill shelter cats.

Author

Hannah Elizabeth Urig, Mississippi State UniversityFollow

Issuing Body

Mississippi State University

Advisor

Smith, David R.

Committee Member

Woodruff, Kimberly A.

Committee Member

Brookshire, Wilson C.

Date of Degree

12-8-2023

Original embargo terms

Campus Access Only 1 Year

Document Type

Graduate Thesis - Campus Access Only

Major

Veterinary & Biomedical Science

Degree Name

Master of Science (M.S.)

College

College of Veterinary Medicine

Department

Department of Pathobiology and Population Medicine

Abstract

Shelters often make euthanasia or adoption decisions based on the results of FeLV-FIV point-of-care tests but given the low estimated prevalence of these diseases and imperfect test performance, this might not be a good practice because of diagnostic error. The objectives of this study were to determine the true prevalence of FeLV and FIV in apparently healthy and sick shelter cats in Mississippi, estimate predictive value of the Zoetis Witness FeLV-FIV Rapid ImmunoMigration test results at the estimated true prevalences through Bayesian latent class modeling, and formulate testing recommendations for shelters. One chapter will review the literature on FeLV and FIV. The bulk of this thesis will focus on determining the true prevalence of retroviral infection in Mississippi shelter cat populations. The last chapter will use Bayesian modeling to estimate test performance and predictive value of test results in healthy and sick shelter cat populations.

Recommended Citation

Urig, Hannah Elizabeth, "Bayesian latent class modeling to evaluate the predictive value of feline leukemia virus and feline immunodeficiency virus testing in apparently healthy and clinically ill shelter cats." (2023). Theses and Dissertations. 5981.
https://scholarsjunction.msstate.edu/td/5981