東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Phylodynamic Methods for Infectious ...

Rasmussen, David A.

Linked to FindBook

Google Book

Amazon

博客來

Phylodynamic Methods for Infectious Disease Epidemiology.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Phylodynamic Methods for Infectious Disease Epidemiology./
Author:	Rasmussen, David A.
Description:	161 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-08(E), Section: B.
Contained By:	Dissertation Abstracts International75-08B(E).
Subject:	Epidemiology. -
Online resource:	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.
LDR:02905nmm a2200289 4500 001 2065308
005 20151130143848.5
008 170521s2014 ||||||||||||||||| ||eng d
020 $a 9781303867026
035 $a (MiAaPQ)AAI3618394
035 $a AAI3618394
040 $a MiAaPQ $c MiAaPQ
100 1 $a Rasmussen, David A. $3 3179993
245 1 0 $a Phylodynamic Methods for Infectious Disease Epidemiology.
300 $a 161 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-08(E), Section: B.
500 $a Adviser: Katia Koelle.
502 $a Thesis (Ph.D.)--Duke University, 2014.
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.
590 $a School code: 0066.
650 4 $a Epidemiology. $3 568544
650 4 $a Statistics. $3 517247
650 4 $a Virology. $3 642304
690 $a 0766
690 $a 0463
690 $a 0720
710 2 $a Duke University. $b Biology. $3 1018710
773 0 $t Dissertation Abstracts International $g 75-08B(E).
790 $a 0066
791 $a Ph.D.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3618394