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Improving HIV Models with Better Ana...

Beck, Ekkehard.

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Improving HIV Models with Better Analysis and Simulation of Dynamic Networks.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Improving HIV Models with Better Analysis and Simulation of Dynamic Networks./
作者:	Beck, Ekkehard.
面頁冊數:	322 p.
附註:	Source: Dissertation Abstracts International, Volume: 77-10(E), Section: B.
Contained By:	Dissertation Abstracts International77-10B(E).
標題:	Industrial engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10117224
ISBN:	9781339786278

Improving HIV Models with Better Analysis and Simulation of Dynamic Networks.
Beck, Ekkehard.

Improving HIV Models with Better Analysis and Simulation of Dynamic Networks. - 322 p.

Source: Dissertation Abstracts International, Volume: 77-10(E), Section: B.

Thesis (Ph.D.)--Northwestern University, 2016.

HIV remains an epidemic despite tremendous prevention efforts and advancements in treatment, thus calling for more and improved prevention interventions. In this thesis, we present two new modelling approaches and three theoretical advancements to improve the modelling and analysis of HIV in order to allow for a better understanding and improved design of HIV prevention interventions. First, an ordinary differential equation (ODE) model is introduced to study the impact of extended high viremics (EHV), i.e. HIV infected individuals who maintain a high viral load for a longer period than usual after the initial infection, on the HIV spread in South Africa. Second, a data-driven agent-based dynamic network simulation model is introduced to study HIV, gonorrhea (NG), and chlamydia (CT) transmission among young men who have sex with men (YMSM) in Chicago. In the theoretical part of this thesis, a class of continuous-time Markov Chaim (CTMC)-based dynamic network models is introduced where the steady-state distribution can be represented using an exponential random graph model (ERGM) distribution. Further, we establish a theoretical framework for the temporal sampling design in longitudinal social network studies which allows for the derivation of the optimal time in between two cross-sectional network samples. Finally, we present an elementary approach to prove that the fraction of susceptible and infected of the susceptible-infected-susceptible (SIS) and susceptible-infected-recovered (SIR) stochastic processes on a complete network converge to the corresponding solutions of the mean-field ODE models.

ISBN: 9781339786278Subjects--Topical Terms:

526216
Industrial engineering.

Improving HIV Models with Better Analysis and Simulation of Dynamic Networks.
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500 $a Adviser: Benjamin Armbruster.
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