Graduate Thesis Or Dissertation

 

Integrative risk analysis of vector-borne disease Público Deposited

Conteúdo disponível para baixar

Baixar PDF
https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/td96k5567

Descriptions

Attribute NameValues
Creator
Abstract
  • In this dissertation I explore the application of two novel modeling techniques for improving risk analysis of vector-borne disease and discuss their potential use in integrating environmental risk assessment that guides environmental and public health decisions. Techniques for analyzing risk have been considered inadequate due to a lack of understanding of the problem and an appropriate analytic-deliberative process clarifying the meaning of analytic findings and uncertainty (National Research Council (NRC), 1996; Peterman and Anderson, 1999). Thus, new integrative risk analysis tools are needed that are responsive to more complex environmental problems. In this work, I develop a qualitative community model that combines a conventional biomathematical model of vector-borne disease transmission with recent developments in community modeling. My procedure predicts the change in risk of vector-borne disease from press perturbations, a disturbance that results in a permanent change in a growth parameter. I also use a Relational Bayesian Modeling technique to exploit existing data to determine plausible mechanisms and geospatial and temporal patterns of disease spread. I apply these tools to Lyme disease and West Nile Encephalitis as examples of two different vector-borne diseases associated with complex ecological communities. Both the qualitative modeling and Bayesian methods provide an integrated risk analysis framework that identifies relationships important in the system and thus, guide the application of quantitative models or provide sufficient information for management decisions.
License
Resource Type
Date Available
Date Issued
Degree Level
Degree Name
Degree Field
Degree Grantor
Commencement Year
Advisor
Committee Member
Academic Affiliation
Non-Academic Affiliation
Subject
Declaração de direitos
Publisher
Peer Reviewed
Language
Digitization Specifications
  • File scanned at 300 ppi (Monochrome) using ScandAll PRO 1.8.1 on a Fi-6670 in PDF format. CVista PdfCompressor 4.0 was used for pdf compression and textual OCR.
Replaces

Relações

Parents:

This work has no parents.

Em Collection:

Itens