Modeling uncertainty in pathogen transmission and evolution for infectious disease management
Creators: name: Castro, Lauren Ann
name: 0000-0002-9778-570X
Publisher: name: The University of Texas at Austin
Year: 2019
Subjects: Computational epidemiology
Evolution
Infectious diseases
Simulation

description: Public health practitioners rely on epidemiological case count and molecular sequence data for making decisions regarding the prevention, control, and treatment of infectious disease. The relationship between what practitioners can observe and what they wish to know, (i.e. the epidemiological or evolutionary state of the population) can be uncertain because of gaps or biases in the data collection and interpretation steps. In this dissertation, I integrate simulations of disease dynamics with statistical frameworks to link such observational data with probabilistic statements about the range of underlying possible outcomes. Chapter 2 addresses the need for real-time estimates of local Zika epidemic risk during an unfolding outbreak to inform county-specific public health response plans. In this chapter, I present a quantitative framework for estimating real-time ZIKV risk that captures uncertainty in case reporting, importations, and vector-human transmission dynamics. I find that accurate estimates of the case reporting rate can reduce the uncertainty in perceived epidemic risk. In addition, local differences in both the environmental suitability for ZIKV transmission and in the numbers of new cases arriving influence how long a policy maker can wait before implementing response efforts to curb a growing epidemic. In Chapter 3, I address the theoretical and practical questions of how early can population level antigenic evolution of seasonal influenza A/H3N2 be predicted, and what are the best data metrics to inform such predictive models. Using a detailed simulation model of seasonal influenza transmission and evolution, I fit predictive logistic regression models of antigenic variant success to epidemiological and population genetic predictors derived from theoretical case and molecular data. The results show that the relative transmission rates of newly emerging influenza variants can robustly indicate future epidemic threats, up to 10 months prior to their widespread expansion. This chapter demonstrates that the early detection of emerging influenza viruses is limited by a tight race between the typical dynamics of antigenic turnover and the annual timeline for vaccine development. Chapter 4 examines how complex evolutionary processes, such as recombination and latency, leave detectable signals in HIV-1 molecular data when analyzed using methods that assume the absence of these processes. First, I develop a new method for simulating the evolutionary history of a set of molecular sequence samples using ancestral recombination graphs (ARG). Next, I use a new statistical framework for comparing simulated and observed HIV-1 trees. I find evidence that the intensity of within-host evolutionary processes is detectable in binary trees constructed using hierarchical clustering methods. Furthermore, the latent reservoir size is likely to differ between individual patients. This model represents an important advance in the realism of HIV within-in host evolutionary modeling. Altogether, the results in this dissertation demonstrate how effective infectious disease management can be improved by using an interdisciplinary approach of computational epidemiology and statistics to strategically think about complex data collection and interpretation questions.