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RAPID: Data driven mathematical modeling of the shared epidemiology of Zika and other arboviruses across the globe

RAPID: Data driven mathematical modeling of the shared epidemiology of Zika and other arboviruses across the globe
RAPID:全球寨卡病毒和其他虫媒病毒共同流行病学的数据驱动数学模型
批准号:
1642174
负责人:
Derek Cummings
金额:
$20.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-05-01 至 2017-10-31

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中文摘要
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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.
期刊论文(3)
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科研奖励(0)
会议论文
Predicting intensities of Zika infection and microcephaly using transmission intensities of other arboviruses
利用其他虫媒病毒的传播强度预测寨卡病毒感染和小头畸形的强度
DOI: 10.2471/blt.16.174128
发表时间: 2016
期刊: Bulletin of the World Health Organization
影响因子: 11.1
作者: [Rodr?guez-Barraquer, Isabel, Salje, Henrik, Lessler, Justin, Cummings, Derek AT]
通讯作者: Cummings, Derek AT
RAPID: Statistical inference of incidence of SARS-CoV-2 in the US using multiple data streams to identify levels of immunity and the impact of non-pharmaceutical interventions
  • 批准号:
    2223843
  • 项目类别:
    Standard Grant
  • 资助金额:
    $20.0万
  • 财政年份:
    2022
  • 负责人:
    Derek Cummings
  • 依托单位:
Doctoral Dissertation Research: Using Phylogeography To Understand the Spatiotemporal Clustering of Dengue Cases in Bangkok
  • 批准号:
    1202983
  • 项目类别:
    Standard Grant
  • 资助金额:
    $1.2万
  • 财政年份:
    2012
  • 负责人:
    Derek Cummings
  • 依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
Data-driven Recommendation System Construction of an Online Medical Platform Based on the Fusion of Information
Development of a Linear Stochastic Model for Wind Field Reconstruction from Limited Measurement Data
  • 批准号:
    --
  • 项目类别:
    --
  • 资助金额:
    40万元
  • 批准年份:
    2020
  • 负责人:
    Vikrant Gupta
  • 依托单位:
基于Linked Open Data的Web服务语义互操作关键技术
  • 批准号:
    61373035
  • 项目类别:
    面上项目
  • 资助金额:
    77.0万元
  • 批准年份:
    2013
  • 负责人:
    冯志勇
  • 依托单位: