| 项目编号: | 1642174
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| 项目名称: | RAPID: Data driven mathematical modeling of the shared epidemiology of Zika and other arboviruses across the globe |
| 作者: | Derek Cummings
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| 承担单位: | University of Florida
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| 批准年: | 2016
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| 开始日期: | 2016-05-01
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| 结束日期: | 2017-10-31
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| 资助金额: | 199991
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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: | zika
; other arbovirus
; zika transmission
; different arbovirus
; zika virus
; mathematical modeling
; zika public health emergency
; project
; rapid award
; shared epidemiology
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| 英文摘要: | This RAPID award will develop estimates of the transmission capability and potential global burden of infection of the Zika virus. These estimates are urgently needed but difficult to obtain because of inaccuracies in blood tests and under reporting of infections. Using existing serum samples together with mathematical modeling, the project will fill knowledge gaps about i) transmission parameters for Zika, ii) the consistency of surveillance approaches, and iii) the global risk of Zika transmission. This approach will also capture information about other viral infections (e.g., chikungunya and dengue) and the potential for future spread. Results from this project will be relevant to the Zika public health emergency, and the researchers have set in place mechanisms to share quality-assured interim and final data as rapidly and widely as possible, including with public health and research communities.
This project will use mathematical models informed by sample serology to estimate the transmission potential for Zika across the globe. Currently, the utility of inference is limited with surveillance reports alone and traditional serological methods, the latter because of cross-reactivity between arboviruses. This project will use a new low-cost, high throughput assay to test for the historic exposure of different antibodies for Zika and other arboviruses. It will compare the rate susceptible individuals in communities that acquire different arboviruses over time. From these results, geostatistical models to predict the force of infection will be developed and risk maps for Zika and other arboviruses will be validated. This knowledge will be used to characterize the shared epidemiology of arboviral diseases around the world. |
| 资源类型: | 项目
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/92480
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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: | |
Derek Cummings. RAPID: Data driven mathematical modeling of the shared epidemiology of Zika and other arboviruses across the globe. 2016-01-01.
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