| 项目编号: | 1701069
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| 项目名称: | DISSERTATION RESEARCH: Using dynamic network models to reveal how heterogeneity in behavioral and immune competence impact disease dynamics in an emerging wildlife disease |
| 作者: | Meggan Craft
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| 承担单位: | University of Minnesota-Twin Cities
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| 批准年: | 2017
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| 开始日期: | 2017-06-01
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| 结束日期: | 2018-05-31
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| 资助金额: | 15620
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| 资助来源: | US-NSF
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| 项目类别: | Standard Grant
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| 国家: | US
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| 语种: | 英语
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| 特色学科分类: | Biological Sciences - Environmental Biology
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| 英文关键词: | infectious disease
; disease
; disease spread
; infection disease
; research
; disease dynamics
; disease model
; wildlife
; immune competence
; empirical contact network datum
; dynamic network
; theoretical model
; wildlife disease
; wildlife system
; first model
; mathematical model
; epidemic dynamics
; wildlife source
; wildlife population
; modeling approach
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| 英文摘要: | Emerging infectious diseases are diseases that appear in new hosts or in a new place. They can result when new strains of a pathogen arise, or from a pathogen being introduced into a new area, and can threaten wildlife, livestock, and humans through illness and death. Because most emerging infectious diseases that shift to humans come from wildlife it is important to understand wildlife diseases. Often, scientists use mathematical models of disease as tools to understand existing patterns of how a disease spreads or to help predict future trends. This research focuses on understanding differences among hosts in how they behave, or how their bodies respond to infection affect the next step in a disease epidemic: when the pathogen spreads among hosts. This research will help scientists better understand how individual differences in what happens after infection should be incorporated into disease models. With this information, scientists and managers will be able to better design data collection and disease models for the targeted control of emerging infectious diseases. The project will also train a graduate student and result in the development of new teaching tools for grade school and undergraduate students, including a website that could be used at many other colleges and universities.
Given the potential for severe consequences of emerging infections diseases with wildlife sources and the time and resource-intensive nature of gathering pathogen data in wildlife populations, improved disease models are necessary tools for providing insight into the research effort necessary to capture the transmission process. The objective of this research is to link empirical and modeling approaches to better understand how among-host variation in traits affects pathogen transmission in a wildlife system. It will integrate a theoretical model with empirical data from the host-pathogen system of house finches and their bacterial pathogen, Mycoplasma gallisepticum. This work will address how the behavioral and physiological phenotypes affect disease dynamics and will also explore the effects of infection-induced behavioral changes. Experimental M. gallisepticum infection data combined with empirical contact network data will be used to parameterize a dynamic network disease model. This research will provide novel insights into the potential role of covariation between behavioral and immune competence in epidemic dynamics, and will serve as one of the first models to apply dynamic networks to an emerging infectious disease system in wildlife. |
| 资源类型: | 项目
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/90138
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| Appears in Collections: | 全球变化的国际研究计划
科学计划与规划
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| Recommended Citation: | |
Meggan Craft. DISSERTATION RESEARCH: Using dynamic network models to reveal how heterogeneity in behavioral and immune competence impact disease dynamics in an emerging wildlife disease. 2017-01-01.
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