Network models and infectious disease control : analysis and insights
Abstract/Contents
- Abstract
- Many models of infectious disease do not explicitly consider the underlying contact network through which the disease spreads. However, network structure can greatly influence the dynamics of an epidemic and has implications for infectious disease control policies. This dissertation explores two important themes of network science as it relates to infectious disease control policy: first, how to incorporate network structure into the modeling and evaluation of infectious disease control policies; and second, how to leverage network structure in the design of optimal control policies. Complete network information is rarely available. Furthermore, even when network data is available, it generally only represents a snapshot in time and does not capture population or network dynamics (e.g., the formation and dissolution of contacts). In Chapter 2, we address these data limitations and describe a general simulation framework for modeling the spread of infectious disease in a dynamic contact network. We provide a methodology for inferring important model parameters, such as those governing network structure and network dynamics, from readily available data sources. In Chapter 3, we present an application of this framework in evaluating the effectiveness and cost-effectiveness of mass media campaigns aimed at reducing concurrent sexual partnerships in sub-Saharan Africa for HIV prevention. In Chapter 4, we address the problem of leveraging network structure in designing infectious disease control policies. In particular, we consider the problem of identifying which links to remove from a contact network in order to maximize the number of individuals who are protected from infection. We show that this problem can be posed as a non-convex quadratically-constrained quadratic program (QCQP), from which a link removal algorithm can be derived. Evaluation of the QCQP algorithm on standard network models demonstrates that it exhibits near-optimal performance and outperforms other intuitive link removal algorithms, such as removing links in order of edge centrality.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2012 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Enns, Eva Andrea | |
|---|---|---|
| Associated with | Stanford University, Department of Electrical Engineering | |
| Primary advisor | Brandeau, Margaret L | |
| Thesis advisor | Brandeau, Margaret L | |
| Thesis advisor | Bambos, Nicholas | |
| Thesis advisor | Johari, Ramesh, 1976- | |
| Advisor | Bambos, Nicholas | |
| Advisor | Johari, Ramesh, 1976- |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Eva A. Enns. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2012. |
| Location | electronic resource |
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