Felix Pabon-Rodriguez, Ph.D., Biostatistics and Health Data Science, Indiana University School of Medicine

"Statistical Modeling of Vector-Borne Diseases and Immune Responses"

The host immune system plays a vital role in controlling and eliminating pathogens during an infection. However, modeling the intricacies of the immune response presents numerous challenges. While there are many mathematical and statistical models that account for various immune processes, a critical gap remains in understanding how within-host disease progression interacts with population-level transmission dynamics. Bridging this gap is important to fully understand the complexities of infectious diseases. We begin by exploring a joint model of longitudinal and survival data for Leishmania infection, incorporating key drivers such as pathogen load, antibody levels, and disease status. Additionally, data on CD4+ and CD8+ T cells are adapted to represent inflammatory and regulatory immune factors, utilizing a cohort study of dogs naturally exposed to Leishmania infantum. The model characterizes relationships between longitudinal biomarkers and time to death from progressive infection, offering insights into individual trajectories of Canine Leishmaniosis (CanL) progression. This within-host model provides a foundational understanding applicable to complex chronic diseases like Visceral Leishmaniasis (VL). A second project involves the application of Bayesian capture-recapture models to estimate population sizes of species within study sites and joint models for estimating the infection prevalence of Lyme disease. Another interesting project focuses on the statistical analysis and spatiotemporal distribution of cases of anemia and its correlation with the risk of Malaria in Africa. To conclude, this presentation introduces an integrative approach that seamlessly translates within-host immunological processes to the population level. The proposed framework has the potential to transform our understanding of infectious disease dynamics, offering insights applicable across different diseases and regions.

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