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Phylodynamic Methods for Infectious ...

Rasmussen, David A.

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Phylodynamic Methods for Infectious Disease Epidemiology.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Phylodynamic Methods for Infectious Disease Epidemiology./
作者:	Rasmussen, David A.
面頁冊數:	161 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-08(E), Section: B.
Contained By:	Dissertation Abstracts International75-08B(E).
標題:	Epidemiology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3618394
ISBN:	9781303867026

Phylodynamic Methods for Infectious Disease Epidemiology.
Rasmussen, David A.

Phylodynamic Methods for Infectious Disease Epidemiology. - 161 p.

Source: Dissertation Abstracts International, Volume: 75-08(E), Section: B.

Thesis (Ph.D.)--Duke University, 2014.

In this dissertation, I present a general statistical framework for phylodynamic inference that can be used to estimate epidemiological parameters and reconstruct disease dynamics from pathogen genealogies. This framework can be used to fit a broad class of epidemiological models, including nonlinear stochastic models, to genealogies by relating the population dynamics of a pathogen to its genealogy using coalescent theory. By combining Markov chain Monte Carlo and particle filtering methods, efficient Bayesian inference of all parameters and unobserved latent variables is possible even when analytical likelihood expressions are not available under the epidemiological model. Through extensive simulations, I show that this method can be used to reliably estimate epidemiological parameters of interest as well as reconstruct past disease dynamics from genealogies, or jointly from genealogies and other common sources of epidemiological data like time series. I then extend this basic framework to include different types of host population structure, including models with spatial structure, multiple-hosts or vectors, and different stages of infection. The later is demonstrated by using a multistage model of HIV infection to estimate stage-specific transmission rates and incidence from HIV sequence data collected in Detroit, Michigan. Finally, to demonstrate how the approach can be used more generally, I consider the case of dengue virus in southern Vietnam. I show how earlier phylodynamic inference methods fail to reliably reconstruct the dynamics of dengue observed in hospitalization data, but by deriving coalescent models that take into consideration ecological complexities like seasonality, vector dynamics and spatial structure, accurate dynamics can be reconstructed from genealogies. In sum, by extending phylodynamics to include more ecologically realistic and mechanistic models, this framework can provide more accurate estimates and give deeper insight into the processes driving infectious disease dynamics.

ISBN: 9781303867026Subjects--Topical Terms:

568544
Epidemiology.

Phylodynamic Methods for Infectious Disease Epidemiology.
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520 $a In this dissertation, I present a general statistical framework for phylodynamic inference that can be used to estimate epidemiological parameters and reconstruct disease dynamics from pathogen genealogies. This framework can be used to fit a broad class of epidemiological models, including nonlinear stochastic models, to genealogies by relating the population dynamics of a pathogen to its genealogy using coalescent theory. By combining Markov chain Monte Carlo and particle filtering methods, efficient Bayesian inference of all parameters and unobserved latent variables is possible even when analytical likelihood expressions are not available under the epidemiological model. Through extensive simulations, I show that this method can be used to reliably estimate epidemiological parameters of interest as well as reconstruct past disease dynamics from genealogies, or jointly from genealogies and other common sources of epidemiological data like time series. I then extend this basic framework to include different types of host population structure, including models with spatial structure, multiple-hosts or vectors, and different stages of infection. The later is demonstrated by using a multistage model of HIV infection to estimate stage-specific transmission rates and incidence from HIV sequence data collected in Detroit, Michigan. Finally, to demonstrate how the approach can be used more generally, I consider the case of dengue virus in southern Vietnam. I show how earlier phylodynamic inference methods fail to reliably reconstruct the dynamics of dengue observed in hospitalization data, but by deriving coalescent models that take into consideration ecological complexities like seasonality, vector dynamics and spatial structure, accurate dynamics can be reconstructed from genealogies. In sum, by extending phylodynamics to include more ecologically realistic and mechanistic models, this framework can provide more accurate estimates and give deeper insight into the processes driving infectious disease dynamics.
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