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

这项快速奖将开发关于蚊子位置的新建模工具和数据，用于改进寨卡病毒传播预测。传染病预测的评估对于改进预测和将模型的结果转化为准确的公共卫生战略至关重要。该项目将提供美洲各地埃及伊蚊的蚊子密度估计，埃及伊蚊是传播寨卡病毒的主要蚊子。该项目还将更新人类人口数据，以获得有关寨卡病毒相关小头畸形症的详细预测。这些信息将被政策制定者用于有关资源分配的决策，以改善公共卫生。该项目的结果将与寨卡病毒突发公共卫生事件相关，研究人员已经建立了机制，尽可能快速和广泛地共享有质量保证的临时和最终数据，包括与公共卫生和研究团体共享。该项目将生成蚊子与人的比例的时空地图，以确定病原体传播模型中蚊子种群动态的模式。它将扩展寨卡病毒传播模型，将蚊子数量作为地理限制和季节变化的函数，并结合蚊子的时间动态。该项目将使用年龄-性别结构和特定年龄生育率来改进怀孕和出生数量，以考虑各国之间的差异。这将为寨卡病毒对怀孕人数的减少提供一个基线估计。开发的模型还将纳入登革热和基孔肯雅热病例，以解释寨卡病毒的错误分类，最终比较各种模型，以推断推动疾病发病率时空变化的因素。模型产出将使用户能够获得寨卡病毒、登革热和基孔肯雅热的在线报告病例和按地点估计的发病率，以改进对疾病传播和流行的预测。

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.