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Multiscale Models for Predicting the Effectiveness of Mitigation Efforts in Controlling Vector-Borne Epidemics

Multiscale Models for Predicting the Effectiveness of Mitigation Efforts in Controlling Vector-Borne Epidemics
用于预测控制媒介传播流行病缓解措施有效性的多尺度模型
批准号:
1563531
负责人:
James Hyman
金额:
$43.43万
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 2020-04-30

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中文摘要
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英文摘要
This research project aims to create improved mathematical models for predicting the spread of mosquito-borne diseases. These models can be used to help guide public health workers in improving the effectiveness of intervention strategies for mitigating the impact of these diseases. The project focuses on analyzing approaches that have the potential for optimizing mitigation strategies for controlling chikungunya, dengue fever, and Zika virus. The partial differential equation mosquito-borne disease transmission models under development in this project will account for spatial heterogeneity in the population density of the mosquito and host populations. The research aims to create a new two-sex model that can account for both vertical and horizontal transmission of bacterial control measures, such as using Wolbachia to mitigate the disease spread. The mathematical analysis of these models will include quantifying the uncertainty in the model forecasts for multiple species of mosquitoes.
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RAPID: Creating and Analyzing Hybrid Multiscale Models for Forecasting and Mitigating an Outbreak of the Ebola Virus Disease
  • 批准号:
    1516615
  • 项目类别:
    Standard Grant
  • 资助金额:
    $19.26万
  • 财政年份:
    2014
  • 负责人:
    James Hyman
  • 依托单位:
Modeling the Effectiveness of Interventions in Stopping the Spead of Vector-Borne Diseases
  • 批准号:
    1122666
  • 项目类别:
    Standard Grant
  • 资助金额:
    $26.64万
  • 财政年份:
    2011
  • 负责人:
    James Hyman
  • 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
新型手性NAD(P)H Models合成及生化模拟