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

流感爆发预测：应用数据同化方法来制定...

基本信息

批准号：
8244591
负责人：
JEFFREY L SHAMAN
金额：
$ 31.88万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-09-09 至 2016-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8244591
关键词：
Address Assimilations Behavior Calendar Characteristics Climate Communities Data Diagnosis Diagnostic Discipline Disease Disease Outbreaks Entropy Epidemic Epidemiology Evaluation Evolution Geographic Locations Health Resources Height Humidity Influenza Information Systems Information Theory Intervention Lead Maps Measures Medical Methodology Methods Metric Modeling Modification Online Systems Outcome Persons Policy Maker Population Probability Proxy Public Health Relative (related person)Research Resources Risk Running Schools Seasons Series Severities Shamanism Solutions System Techniques Time Uncertainty United States Variant Weather Work base climate change data modeling design flu transmission improved influenza epidemic influenza outbreak insight interest killings mathematical model member model design models and simulation novel pandemic disease research study skills tool two-dimensional

项目摘要

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

描述（由申请人提供）：将开发用于短期（5天到3个月）基于合奏的流行性流感预测的系统。为了对流感感染成果进行熟练的预测，该系统将采用最新的数据同化技术，类似于在数值天气预测中使用的技术，以将流感感染的实时估计纳入流感动力学的数学模型中。拟议的工作将建立一种便携式，与本地相关的操作疾病预测系统，该预测系统是新颖的，其定量，统计上严格的方法。由于最近对流感感染率的实时，基于Web的估计以及经过观察验证的流感传播动力学模型的存在，该系统是可能的。该项目的目的是设计基于整体的模型/数据同化系统，该系统将这些信息资源汇集在一起，以创建熟练的，概率的流感结果预测。预测系统将使用称为集合调整Kalman滤波器的同化技术开发。要回答的问题包括：基于Web的流感估计值在初始化和约束流感的数学模型中有用？此类模型/数据系统在每周和每月的销售时段可以提供什么水平的可预测性？在疫情高峰期，人群中流感的时间和水平的不确定性范围是什么？在季节中可以评估这些指标的早期？这些问题的答案将决定模型/数据同化系统可以在各个时间尺度上传递的可预测性水平。在其他学科领域的工作表明，使用集合调整Kalman滤波器开发的模型/数据同化系统是可行的，优化模型行为以更好地匹配观察，并提供了一个严格的框架来量化可预测性。假设在一系列交货时间内，将实现对当地流感风险的熟练预测。可预测性的局限性将被明确确定，并将对当地流感风险进行预测。公共卫生相关性：仅在美国，流感估计每年造成35,000人杀死35,000人，并给全球公共卫生带来巨大负担。基于实时数据和环境强迫的本地短期预测将为公共卫生官员提供开发当地适当，及时的干预策略的机会