Inferring Transmission Dynamics through Patterns of Genetic Variation Público

Abstract

For a rapidly evolving population, the pattern of genetic variation is shaped by evolutionary processes and population dynamics, providing a window to understand the underlying dynamics. In the context of infectious disease dynamics, a rapidly increasing number of pathogen genome sequences complements case-based inferences, allowing a better understanding of transmission dynamics. For more robust and reliable genome-based inference, this thesis attempts to further understand genome-based approaches, especially during the early spread of newly emerged viruses. In particular, this thesis focuses on considerations and challenges of phylodynamic inferences during the early spread of newly emerged viruses or variants. I first propose a novel approach to circumvent the phylogenetic uncertainty due to the low level of genetic variation during early spread. Then, I examine the misspecification of generation interval distribution for the early exponential growth phase. Next, I investigate the non-randomness in the dataset from the over-representation of epidemiological clusters, which could intensify when there are fewer sequences available, such as in early outbreaks. Lastly, I revisit the relationship between transmission trees and phylogenies by reviewing the inference approaches that infer transmission trees from phylogenetic trees. Together, this thesis aims to advance our understanding of important considerations in phylodynamic analyses and provides insights for improved implementation and interpretation of these methodologies.

Table of Contents

1. Introduction

2. Epidemiological inference for emerging viruses using segregating sites

2.1 Contribution to the published work

2.2 Published manuscript

2.3 Supplementary information

3. Common misspecification of the generation interval leads to the underestimation of R in phylodynamic inference

3.1 Abstract

3.2 Introduction

3.3 Methods

3.3.1 Model structure for simulating mock datasets

3.3.2 Bayesian phylodynamic analyses

3.4 Results

3.4.1 R is systematically underestimated under a misspecified exponential distribution with true mean

3.4.2 Underestimation of R can be explained by the R − r relationship

3.5 Discussion

3.6 Supplementary information

4 Epidemiologically clustered sequence in phylodynamic inferences

4.1 Abstract

4.2 Introduction

4.3 Methods

4.3.1 Epidemiological and evolutionary simulations

4.3.2 Sampling of viral sequences from simulations

4.3.3 Summary statistics for characterizing the viral sequence datasets

4.3.4 Assessment of bias in phylodynamic inference

4.4 Results

4.4.1 Characteristics of the simulated datasets

4.4.2 Phylodynamic inference

4.4.3 Differences in one-dimensional summary statistics between random sample and non-random sample sequence datasets

4.4.4 Differences in multi-dimensional summary statistics between random sample and non-random sample sequence datasets

4.4.5 Transmission heterogeneity

4.5 Discussion

4.6 Supplementary information

5 Transmission history reconstruction using phylogenies

5.1 Introduction

5.2 Early analyses using pathogen phylogenies to infer transmission history

5.3 Recognizing differences between transmission trees and phylogenetic trees

5.4 Reconstructing transmission trees using phylogenies

5.4.1 Within-unit genetic diversity stemming from de novo mutation

5.4.2 Within-unit diversity stemming from multiple infection and de novo mutation

5.5 Perspectives

5.5.1 Inference methods rely on different assumptions, approaches, and data

5.5.2 Choice of inference method to use should be based on data characteristics

5.5.3 Systematic comparisons are needed to evaluate the performance of inference approaches

5.6 Conclusion

5.7 Supplementary information

5.7.1 Tree representations of transmission history

5.7.2 Reconstructing and dating phylogenetic trees

5.7.3 Supplementary Table

6 Conclusion

About this Dissertation

Rights statement

• Permission granted by the author to include this thesis or dissertation in this repository. All rights reserved by the author. Please contact the author for information regarding the reproduction and use of this thesis or dissertation.

School	Laney Graduate School
Department	Biological and Biomedical Sciences
Subfield / Discipline	Population Biology, Ecology & Evolution
Degree	Ph.D.
Submission	Dissertation
Language	English
Research Field	Biology, Evolution and Development Biology, Bioinformatics Biology, Ecology
Palavra-chave	Infectious Disease Evolutionary Genetics Epidemiology Genomic Epidemiology Genetic diversity Trnasmission Dynamics Phylodynamics
Committee Chair / Thesis Advisor	Katia Koelle, Emory University
Committee Members	Anne Piantadosi, Emory University Rustom Antia, Emory University Daniel Weissman, Emory University Max Lau, Emory University

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