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A Bayesian Latent Class Gamma Mixture Model to Identify Clinical Visceral Leishmaniasis (309906)

Keywords: Bayesian, latent class model, infectious disease, visceral leishmaniasis

Leishmaniasis is a potentially deadly parasitic disease that places hundreds of millions of people and domesticated dogs at risk worldwide. Like many infectious diseases, there is no practical gold standard for diagnosing clinical visceral leishmaniasis (VL). Latent class modeling (LCM) has been proposed to estimate clinical disease. Typically, these proposed models have leveraged information from diagnostic tests with dichotomous serological and PCR assays and have not employed continuous diagnostic test information. We employ Bayesian LCMs to improve the identification of VL using the dichotomous PCR assay and the Dual Path Platform (DPP®) serology test. Though historically used as a dichotomous assay, DPP® can also yield numerical information via the DPP® reader. Using data collected from a cohort of hunting dogs across the United States, which were identified as having either negative or symptomatic clinical status, we evaluate the impact of including numerical DPP® reader information as a proxy for immune response. We find that inclusion of DPP® reader information via a Gamma mixture model reduces uncertainty in identifying latent disease state.