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

描述(申请人提供)：将开发一个系统，用于生成基于集合的短期(5天至3个月)流行性流感预测。为了巧妙地预测流感感染结果，该系统将应用最先进的数据同化技术，类似于数值天气预报中使用的技术，将对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可移植的、与当地相关的可操作的疾病预测系统，该系统在量化和统计严谨的方法上是新颖的。这一系统之所以可能，是因为最近出现了对流感感染率的实时、基于网络的估计，以及存在经观测验证的流感传播动力学模型。这个项目的目的是设计一个基于集合的模式/数据同化系统，将这些信息资源聚集在一起，创建对流感结果的熟练的、概率的预测。预报系统将使用一种被称为集合调整卡尔曼滤波的同化技术来开发。需要回答的问题包括：基于网络的流感估计对于初始化和约束流感的数学模型有多大用处？这样的模型/数据系统在每周和每月的交付期可以提供什么水平的可预测性？在暴发高峰期，人群中流感的时间和水平的不确定范围是什么，以及在季节的早期可以评估这些指标吗？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。其他学科领域的工作表明，使用集合调整卡尔曼滤波开发的模式/数据同化系统是可行的，优化了模式行为以更好地匹配观测，并为量化可预测性提供了严格的框架。据推测，对局部流感风险的巧妙预测将在一系列准备时间内实现。将明确确定可预测性的界限，并对局部流感风险进行预测。 与公共卫生相关：仅在美国，每年就有大约35,000人死于流感，给全世界的公共卫生带来巨大负担。基于实时数据和环境压力的当地短期预测将为公共卫生官员提供机会，以制定适合当地情况的及时干预战略，能够评估不断发展的流行病的严重性，并有时间获得更多的医疗和公共卫生资源。

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. PUBLIC HEALTH RELEVANCE: Influenza kills an estimated 35,000 people each year in the United States alone and presents an enormous burden on worldwide public health. Local, short-term predictions based on real-time data and environmental forcing would provide public health officials the opportunity to develop locally appropriate, timely intervention strategies, the ability to gauge the severity of the developing epidemic, and the time to garner additional medical and public health resources