Dissertation
Bayesian approaches to within and between host models for infectious disease processes
University of Iowa
Doctor of Philosophy (PhD), University of Iowa
Spring 2023
DOI: 10.25820/etd.007089
Abstract
The study of communicable diseases, commonly referred to as infectious diseases, is getting serious attention from researchers and health professionals. Such diseases result from the introduction, presence, and proliferation of pathogenic microorganisms within a host. The manifestation of these infections may range in severity going from an asymptomatic stage (no clinical signs) to severe cases, and even death. Two microbes of great concern are Leishmania infantum and Borrelia burgdorferi, which are responsible for causing the vector-borne disease: Leishmania Infection and Lyme disease. Since no human vaccines are currently available for these two diseases, there is a need for a better understanding of how the immune system contributes to the control of infections over the disease course. A hosts im- mune system plays a significant role in managing and clearing pathogen material during an infection, but this complex process presents numerous challenges from a modeling perspective.
In the first application, a Bayesian model is presented for the within-host modeling of Leishmania infection, which considers the interplay between key drivers of the disease process such as pathogen load, a measure of antibody levels, and a high- level categorization of disease progression. The longitudinal model also accommodates additional inflammatory and regulatory immune factors. Besides measuring antibody levels produced by the immune system, we also investigate the effects of CD4+ and CD8+ T cells expression of interleukin 10, interferon gamma, and programmed cell death protein-1 on the disease process. The model is developed using data collected from a cohort of dogs naturally exposed to Leishmania infantum, and the case of co-infection with other pathogens is also considered. The different findings drawn from this analysis and the application to individual-level forecasting are of direct clinical relevance. This work also offers potential opportunities for ap- plication in veterinary practice and provides motivation for additional investigation to better understand and predict disease progression. As an important zoonotic human pathogen, these results may support future efforts to prevent and treat human Leishmaniosis.
The second application presents a Bayesian capture-recapture model of vector- reservoir interaction in an ecological setting, examining a reservoir-targeted vaccine against Borrelia burgdorferi. Lyme disease is transmissible to humans by blood-meal of infected ticks. Since the ticks feed on mice to propagate the infection, mice are recognized to be the main natural reservoir for the disease’s transmission cycle. As a result, ticks can transmit the disease to people. In recent years, reservoir-targeted vaccines are emerging as an effective means to control the spread of zoonotic and vector-borne diseases by tackling the issue at the source with the goal of blocking the path of transmission to humans. In general, this analysis consists of two independent but related sub-models. The first sub-model is the combination of capture-recapture method and a data augmentation technique with the goal of estimating the population sizes of the species of interest at each study field site. The second sub-model implements a Bayesian longitudinal model with autoregressive terms to estimate ecologic and immunopathologic parameters of interest such as the mice and nymphal tick prevalence of Borrelia burgdorferi over the study period. Results obtained from this work could help reduce the risk of human exposure to Lyme disease.
Details
- Title: Subtitle
- Bayesian approaches to within and between host models for infectious disease processes
- Creators
- Felix Manuel Pabon-Rodriguez
- Contributors
- Grant Brown (Advisor)Jacob Oleson (Committee Member)Brian Smith (Committee Member)Christine Petersen (Committee Member)
- Resource Type
- Dissertation
- Degree Awarded
- Doctor of Philosophy (PhD), University of Iowa
- Degree in
- Biostatistics
- Date degree season
- Spring 2023
- DOI
- 10.25820/etd.007089
- Publisher
- University of Iowa
- Number of pages
- xii, 179 pages
- Copyright
- Copyright 2023 Felix M Pabon-Rodriguez
- Translated title
- Enfoques Bayesianos para Modelos Dentro y Entre Huespedes para Procesos de Enfermedades Infecciosas
- Language
- English
- Date submitted
- 04/14/2023
- Date approved
- 06/30/2023
- Description illustrations
- illustrations, tables, graphs
- Description bibliographic
- Includes bibliographical references (pages 169-179).
- Public Abstract (ETD)
- The study of communicable diseases requires serious attention from researchers and health professionals at all scales, from infection prevention to individualized care for patients. In this work, we will focus on two microbes of great public health concern: Leishmania infantum and Borrelia burgdorferi. In the first part, we present a Bayesian model for the within-host dynamics of Leishmania infection using longitudinal data collected from a cohort of dogs naturally exposed to Leishmania infantum. We consider the interplay between key drivers of the disease process, immune responses, and the role of co-infections. The second part will present a Bayesian capture-recapture model addressing vector-reservoir interaction in an ecological setting. This work examines the effect of a reservoir-targeted vaccine against Borrelia burgdorferi, designed to interrupt transmission of the pathogen in the environment. These two analyses are of direct clinical and preventative relevance, presenting possible opportunities for application in veterinary practice and exposure prevention. Results may support future efforts to help reduce the risk of human exposure to Lyme disease and Leishmaniasis.
- Academic Unit
- Biostatistics
- Record Identifier
- 9984425199802771
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