Goal: Bayesian state-space model to predict Canine Leishmaniasis (CanL) progression kinetics based on continuous diagnostic data and discrete clinical stages.
Project Summary: The immune system is a complex network which involves organs, cells, and proteins working together with the main purpose of protecting the body against harmful microorganisms such as bacteria, viruses, fungi, and toxins. To explore and study the responses of the host immune system during the course of a disease, we model the interaction between pathogen load, antibody responses, and the clinical outcome components of this complex system. Specifically, we focused on Canine Leishmaniasis (CanL), a vector-borne disease caused by a parasite, which affects internal organs of the body and is known to be fatal if patients remain untreated. In addition, we also consider the impact of possible co-infections with other diseases, which could increase the complexity of the course of infection and even contribute to different outcomes for infected subjects. With CanL specifically, we consider the presence of Borrelia, Anaplasma, Ehrlichia, and Heartworm. We present a Bayesian Hierarchical Model (BHM) that allows us to define a simple and generalizable set of parameters that predict disease progression by using an individual host level framework. We have learned that one limitation in vaccination strategies is a focus on neutralizing antibodies, without incorporating broader complexities of immune responses. In this work, we explore this complexity by jointly considering the interaction between pathogen and antibody development with the purpose of improving our understanding of the processes of disease progression and natural immunity.
Note: This folder repository contains supplemental materials for the following two manuscripts:
- "Bayesian Multivariate Longitudinal Model for Immune Responses to Leishmania - a tick borne Co-Infection Study". Felix Pabon-Rodriguez, Grant Brown, Breanna M. Scorza, and Christine Petersen.
- "Within-Host Bayesian Joint Modeling of Longitudinal and Time-to-Event Data of Leishmania Infection". Felix Pabon-Rodriguez, Grant Brown, Breanna M. Scorza, and Christine Petersen.