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Phylodynamic and Transmission Network Individual Level Infectious Disease Models

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Title: Phylodynamic and Transmission Network Individual Level Infectious Disease Models
Author: Angevaare, Justin
Department: Department of Mathematics and Statistics
Program: Mathematics and Statistics
Advisor: Feng, ZenyDeardon, Rob
Abstract: The individual level model (ILM) framework of Deardon et al. (2010) outlines the incorporation of individual specific risk factors into infectious disease models. ILMs represent individual-specific disease state transitions, and allow for investigation of hypotheses regarding overall risk to individuals. Such investigations are relevant in the development of projections and control policies while considering population heterogeneity. We extend the ILM framework to allow for competing risks of disease transmission with Transmission Network ILMs (TN-ILMs). The data requirements of TN-ILMs includes the typically latent transmission times, and transmission network, so we present TN-ILMs along Bayesian data augmentation methods to infer TN-ILM parameters jointly with these latent data. Our Markov Chain Monte Carlo based inference strategy for TN-ILMs is implemented in Pathogen.jl, a high performance, and highly flexible statistical software package in the Julia language. Pathogen.jl supports simulation, inference, and visualization of epidemics from Susceptible-Infected (SI), Susceptible-Exposed-Infected (SEI), Susceptible-Infected-Removed (SIR), and Susceptible-Exposed-Infected-Removed (SEIR) TN-ILMs. Applications of TN-ILMs using Pathogen.jl are presented for the 1861 Hagelloch measles outbreak (Pfeilsticker, 1863; Oesterle, 1992) and an experimental tomato spotted wilt virus outbreak (Hughes et al. 1997). We further extend TN-ILMs to full phylodynamic ILMs. Phylodynamics is the combined study of disease spread and evolution. Phylodynamic approaches are most appropriate when dense genetic sampling has been conducted on the pathogen during an outbreak, and evolutionary and epidemiological processes occur on a similar time scale. With the phylodynamic ILM extension, we can jointly infer disease transmission times, transmission network, pathogen phylogeny, and the phylodynamic ILM parameters. We contrast a fully phylodynamic approach to one that incorporates genetic distances as a dyadic covariate in various TN-ILMs, and show that phylodynamic ILMs offer improved event time and transmission network inference, at a significantly increased computational cost.
Date: 2020-09
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International

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Attribution-NonCommercial-NoDerivatives 4.0 International Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International