Browsing by Author "Koelle, Katharina"

Mathematical modeling has proven to be an essential tool for the development of

control strategies and in distinguishing driving factors in disease dynamics. A key

determinant of a given model's potential to aid in such measures is the availability

of data to parameterize and verify the model. For developing countries in particular,

data is often sparse and difficult to collect. It is therefore important to understand

the types of data that are necessary for a modeling project to be successful. In this

thesis I analyze the value of particular types of data for a set of infections. The first

project analyzes the importance of considering age-specific mixing patterns in vaccine

preventable infections in which disease severity varies with age. The second project

uses a simulated data set to explore the plausibility of recovering the parameters of an

epidemiological model from a time series data set of monthly dengue haemorrhagic

fever reports.

RNA viruses are an important cause of global morbidity and mortality. The rapid evolutionary rates of RNA virus pathogens, caused by high replication rates and error-prone polymerases, can make the pathogens difficult to control. RNA viruses can undergo immune escape within their hosts and develop resistance to the treatment and vaccines we design to fight them. Understanding the spread and evolution of RNA pathogens is essential for reducing human suffering. In this dissertation, I make use of the rapid evolutionary rate of viral pathogens to answer several questions about how RNA viruses spread and evolve. To address each of the questions, I link mathematical techniques for modeling viral population dynamics with phylogenetic and coalescent techniques for analyzing and modeling viral genetic sequences and evolution. The first project uses multi-scale mechanistic modeling to show that decreases in viral substitution rates over the course of an acute infection, combined with the timing of infectious hosts transmitting new infections to susceptible individuals, can account for discrepancies in viral substitution rates in different host populations. The second project combines coalescent models with within-host mathematical models to identify driving evolutionary forces in chronic hepatitis C virus infection. The third project compares the effects of intrinsic and extrinsic viral transmission rate variation on viral phylogenies.

The evolution of antigenically variable viruses cannot be understood without studying the interaction between viruses and the host immune system. Viral evolution is driven by their fast acquisition of genetic variation as well as by the strong selection imposed by the host immune response. Moreover, understanding viral evolution dynamics and its interplay on the host immune response can provide essential information for vaccine development. In this dissertation, I use an integrative approach to study various aspects of this interplay in two viral systems: influenza A (IAV) and cytomegalovirus (CMV), both ubiquitous in humans and significant public health threats.

Congenital cytomegalovirus infection is the leading infectious cause of congenital defects. As such, the study of viral dynamics is essential to develop better treatment and prevention procedures. In a monkey challenge study for congenital cytomegalovirus infection, I investigated viral transmission between maternal and fetal compartments. Using high-coverage sequencing data, I examined viral evolutionary dynamics in time and space. I found evidence of large transmission bottleneck sizes between maternal compartments and in congenital transmission. I also inquire about the role of preexistent CMV-specific antibodies in the virus population, finding no apparent effect in the viral genetic make-up but reduced viral load and also reduced congenital transmission.

One of the more promising vaccine formulation for CMV until now is the gB-MF59 vaccine, which is based on a soluble version of the immunodominant gB protein. To understand immune and viral factors contributing to vaccine efficacy for this formulation, I examined immunoglobulin G binding to a gB-specific peptide microarray from seropositive individuals and vaccinees prior to and after vaccination. The antibody profile observed from binding clustered by individual immune exposure history. While the antibody profile elicited by vaccination show high agreement with the one from seropositive individuals, I also identified regions in gB preferentially targeted in vaccinees. Moreover, I observe no difference between the antibody profiles of vaccinees with different clinical outcomes and instead found further evidence for reduced cross-immunity between divergent genotypes.

Original antigenic sin (OAS) refers to the tendency of the host immune system to focus on previously recognized viral epitopes during secondary challenges with related viral strains. This preference is sustained by antibody memory and can result in suboptimal immune protection. Mounting evidence highlights the importance of the initial viral strain encountered during childhood, which primes the antibody repertoire to contend against further infections. Here, the goal was to identify OAS-driven cohort effects in IAV driven by at least one antigenic mutation. Using sequence data and host's age information from individuals infected with H1N1 in the U.S. from 2009 to the present, we searched for potential signatures of birth year cohort changes driven by new variants with nonsynonymous mutations. We identified multiple variants with such properties and studied the age groups with differential abundance after such variant arose.