Abstract

Diagnostic tests often aid in diagnosis of infectious diseases, like the neglected tropical disease visceral leishmaniasis (VL). While such tests provide useful information, they are imperfect, so they can produce false positive or false negative results. Moreover, diagnostic tests for VL are often dichotomous, meaning they only provide positive or negative results. The Dual Path Platform (DPP) serology test has been used as a dichotomous assay, but can also yield numerical information via the DPP reader. Although we can only observe diagnostic test results, rather than the true disease status of an individual, we can use a set of statistical methods called latent class models to estimate the true disease status. We can also incorporate the numerical information from the DPP reader in latent class models. In this paper, we evaluate the impact of including this numerical information in a Bayesian latent class model to provide additional information about immune response. We compare this to a more traditional latent class model, which only incorporates dichotomized assay information. We fit these models to data collected from a cohort of hunting dogs across the United States. Incorporating DPP reader information allows us to illustrate changes in immune response for different ages.

For the full article, please visit https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0010236