| 项目编号: | 1547168
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| 项目名称: | EAGER: New genomic resources and models for predicting evolving vector-borne disease dynamics in a changing world |
| 作者: | Dina Fonseca
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| 承担单位: | Smithsonian Institution
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| 批准年: | 2014
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| 开始日期: | 2015-07-15
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| 结束日期: | 2017-06-30
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| 资助金额: | USD130000
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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: | vector-borne disease
; model
; avian malaria
; disease
; vector
; disease dynamics
; climate change
; functional model
; vector-borne
; predictive model
; disease prevalence
; disease system
; evolution
; new way
; model methodology
; response
; host
; ideal disease system
; tolerance
|
| 英文摘要: | Global climate change has accelerated our need to understand disease dynamics. Diseases transmitted among hosts by small invertebrates such as mosquitoes or ticks (vectors) are on the rise across the world but links to climate change are unclear. Climate change can impact vector-borne disease transmission directly by shifting the occurrence of competent hosts and vectors, or a parasite, or more subtly by changing the timing or nature of their interaction. Predicting the response of vector-borne diseases to climate change requires both an understanding of how all the species involved are likely to be affected as well as new ways to identify and predict how they interact and furthermore how they and their interactions may evolve. This research will develop and test functional models of vector-borne diseases that incorporate co-evolutionary change. Specifically, the project will develop models that account for past and predict future evolution of responses to avian malaria, a mosquito borne disease, using a database of disease prevalence in the endangered Hawaiian honeycreepers. This research will answer important questions in epidemiology by measuring and integrating evolutionary changes in hosts, vectors and parasites into predictive models of disease dynamics under future climate scenarios.
An ideal disease system in which to develop and train such a model is avian malaria in native and introduced Hawaiian birds. The agent of avian malaria in Hawaii is a non-native Haemosporidian parasite, Plasmodium relictum, vectored by mixes of two non-native strains of the mosquito Culex quinquefasciatus. Avian malaria in Hawaii occurs as a series of replicated natural experiments in which vector and parasite prevalence vary along elevational gradients on several islands, and a parallel gradient in tolerance among some bird hosts has been reported. Although P. relictum was previously highly virulent to all Hawaiian honeycreepers, evolution of tolerance (or resistance) has been observed in several species such as the Amakihi (Hemignatus sp). Within- and among-species differences in the degree of tolerance suggest that genomic variation underlie such differences; moreover, there is geographic variation within and across islands in host species composition, host tolerance, climate (temperature and precipitation), vector abundance, vector competence, and pathogen fitness. This system has been studied for several decades; thus, many of the host and vector demographic characteristics are well understood. Specifically this project will (1) use a machine learning approach to develop ways to correlate the genetic data from the relevant species with the ecological, environmental, and epidemiological pressures that might shape their evolution; (2) analyze past and current parasite diversity using Next Generation genomics to be fed into the model. Though constructed with reference specifically to the Hawaiian Plasmodium system, if successfully validated, this model methodology will then be available for broad application into any disease system in which evolution is expected to occur in response to shifting climatological, environmental, or ecological conditions. |
| 资源类型: | 项目
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/93998
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| Appears in Collections: | 影响、适应和脆弱性
气候减缓与适应
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| Recommended Citation: | |
Dina Fonseca. EAGER: New genomic resources and models for predicting evolving vector-borne disease dynamics in a changing world. 2014-01-01.
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