RAPID: Overcoming uncertainty to enable estimation and forecasting of Zika virus transmission

RAPID：克服不确定性以实现寨卡病毒传播的估计和预测

• 批准号: 1641130
• 负责人: Alex Perkins
• 金额: $ 20万
• 依托单位: University of Notre Dame
• 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
• 起止时间: 2016-05-01 至 2018-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1641130&HistoricalAwards=false
• 关键词: RAPID; Overcoming; uncertainty; enable; estimation; RAPID; Overcoming; uncertainty; enable; estimation

This RAPID award will develop new modeling tools and data on mosquito locations that will be use to improve Zika transmission forecasting. The assessment of infectious disease forecasts is critical for improving predications and translating the results from the models into accurate public health strategies. This project will provide estimates of mosquito density across the Americas for Aedes aegypti, the primary mosquito that transmits Zika. The project also will update human population data for detailed predictions about Zika-associated microcephaly. This information will be used by policymakers for decisions concerning resource allocation to improve public health. 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 generate spatiotemporal maps of mosquito-to-human ratios to determine patterns of mosquito population dynamics for pathogen transmission models. It will expand Zika transmission modeling to consider mosquito abundance as a function of geographic limits and seasonal changes combined with temporal dynamics for mosquitos. The project will refine pregnancies and birth counts using age-sex structure and age-specific fertility rates to account for variation within countries. This will provide a baseline estimate of what reduction Zika has on the numbers of pregnancies. The model developed will also incorporate dengue and chikungunya cases to account for Zika misclassification, ultimately comparing models for inferring factors that drive spatial and temporal variation in disease incidence. Model outputs will allow users to obtain online reported cases and estimated incidences by location for Zika, dengue, and chikungunya to improve forecasts of disease transmission and prevalence.

这项快速奖项将开发有关蚊子位置的新建模工具和数据，这些工具将用于改善寨卡传播的预测。对传染病预测的评估对于改善谓词并将结果从模型转化为准确的公共卫生策略至关重要。该项目将为整个美洲的蚊子密度估计，埃及埃及（Aedes Aegypti）是传播Zika的主要蚊子。该项目还将更新人口数据，以获取有关寨卡相关小头畸形的详细预测。这些信息将由政策制定者使用有关资源分配以改善公共卫生的决策。该项目的结果将与Zika公共卫生紧急情况有关，研究人员已经建立了尽可能快速和广泛地共享质量鉴定的临时和最终数据的机制，包括与公共卫生和研究社区。该项目将在蚊子到 - 人类与人类的时空图生成蚊子的比例，以确定蚊子群体的传播模型。它将扩大Zika传播建模，以将蚊子丰度视为地理极限和季节性变化的函数，并结合蚊子的时间动力学。该项目将使用年龄性结构和特定年龄的生育率来完善怀孕和出生计数，以解释国家内部的差异。这将提供一个基线估计，以减少寨卡病毒对怀孕的数量。开发的模型还将纳入登革热和基孔肯雅病例，以解释寨卡病毒错误分类，最终比较了推断驱动空间和时间变化疾病发生率的因素的模型。模型输出将允许用户获得在线报告的病例和按位置估计的Zika，Dengue和Chikungunya的发生率，以改善疾病传播和患病率的预测。