Influenza Outbreak Prediction: Applying Data Assimilation Methodology to Make...

描述（由申请人提供）：将开发一个系统，用于生成流行性流感的短期（5天至3个月）整体预测。为了熟练地预测流感感染的结果，该系统将采用最先进的数据同化技术，类似于数值天气预测中使用的技术，将流感感染的实时估计纳入流感传播动力学的数学模型。拟议的工作将建立一个便携式的，与当地相关的业务疾病预测系统，在其定量，统计严格的方法是新颖的。这一系统是可能的，由于最近出现的实时，基于网络的流感感染率估计和存在的观测验证模型的流感传播动力学。该项目的目的是设计一个基于集成的模型/数据同化系统，将这些信息资源结合在一起，以创建熟练的，概率预测流感的结果。预报系统将使用一种称为集合调整卡尔曼滤波器的同化技术。需要回答的问题包括：如何有用的是基于网络的流感估计初始化和约束数学模型的流感？这种模型/数据系统在每周和每月的交货期内可以提供什么程度的可预测性？在流感爆发高峰期，人群中流感爆发的时间和水平的不确定性界限是什么？在流感季节的多早时间可以评估这些指标？对这些问题的回答将决定模式/数据同化系统在不同时间尺度上所能提供的可预测性水平。在其他学科领域的工作表明，模式/数据同化系统开发使用集合调整卡尔曼滤波器是可行的，优化模式的行为，以更好地匹配观测，并提供了一个严格的框架量化的可预测性。据推测，当地流感风险的熟练预测将在一系列的提前时间内实现。将明确确定可预测性的限度，并对当地流感风险进行预测。

DESCRIPTION (provided by applicant): A system for generating short-term (5-day to 3 month) ensemble-based predictions of epidemic influenza will be developed. To make skillful forecasts of influenza infection outcomes, this system will apply state-of the-art data assimilation techniques, similar to those used in numerical weather prediction, to incorporate real-time estimates of influenza infection into mathematical models of influenza transmission dynamics. The proposed work will establish a portable, locally relevant operational disease forecast system that is novel in its quantitative, statistically rigorous approach. This system is possible due to the recent advent of real-time, web-based estimates of influenza infection rates and the existence of observationally validated models of influenza transmission dynamics. The aim of this project is the design of an ensemble-based model/data assimilation system that brings these informational resources together to create skillful, probabilistic forecasts of influenza outcomes. The forecast system will be developed using an assimilation technique called the ensemble adjustment Kalman filter. Questions to be answered include: How useful are web-based influenza estimates for initializing and constraining mathematical models of influenza? What levels of predictability can such model/data systems deliver at weekly and monthly lead-times? What are the uncertainty bounds on the timing and level of influenza in a population at the height of an outbreak, and how early in the season can these metrics be evaluated? Answers to these questions will determine the levels of predictability the model/data assimilation system can deliver at various time scales. Work in other discipline fields has demonstrated that model/data assimilation systems developed using the ensemble adjustment Kalman filter are practicable, optimize model behavior to better match observations, and provide a rigorous framework for quantifying predictability. It is hypothesized that skillful prediction of local influenza risk will be realized over a range of lead times. The limits of predictability will be explicitly detemined, and forecasts of local influenza risk will be made.