Influenza Forecasting Center of Excellence at University of Massachusetts Amherst

马萨诸塞大学阿默斯特分校流感预测卓越中心

• 批准号: 10219788
• 负责人: Nicholas G Reich
• 金额: $ 95万
• 依托单位: UNIVERSITY OF MASSACHUSETTS AMHERST
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10219788
• 关键词: Influenza; Forecasting; Center; Excellence; University

PROJECT SUMMARY/ABSTRACT The central goal of this project is to serve as a strong, interdisciplinary center of forecasting research and science. We will achieve this through innovation in development of forecasting methodologies and systematic research into optimal the communication and visualization of forecasts. By working with the US Centers for Disease Control and Prevention in this cooperative agreement, we aim to extend existing methodologies to incorporate new data sources and model structures, thereby improving influenza forecast accuracy in the US. We will review and revise existing FluSight forecasting guidance, targets, and accuracy evaluation at the national, regional, and state levels. This will include conducting stakeholder interviews with federal, state, and local epidemiologists to track uses of forecasts and outcomes and identifying unmet needs from current forecasts. We will develop and refine methods to create forecast ensembles, with a specific focus on developing ensemble weighting schemes using robust, penalized methods to estimate model weights. We will identify methodologies and data sources that increase forecast accuracy for start and peak week forecasts, peak intensity, and short-term forecasts at the national, regional, and state level. Our work here will focus on developing multi-scale spatial models that leverage state and zip-code level data on influenza infections from public and private sources. We will develop communication products and methods to describe forecast results and uncertainty for federal and state public health officials and the public. We will achieve this by incorporating new visualizations into our existing interactive data visualization product for influenza forecasts in the US and studying systematically the end-user perception of various visualization and data presentation layouts. Finally, we will develop and adapt successful seasonal methodologies, data sources, and communication approaches for forecasting the timing, intensity, and short-term trajectory of an emerging influenza pandemic. Specifically, we will create, test, and disseminate weekly data summaries and visualizations from the most up-to-date sources of reported influenza cases in the US (including data from our real-time point-of-care data sources), and validate our new spatial models against simulated pandemic data.

项目总结/摘要 该项目的中心目标是成为一个强大的跨学科预测研究中心 和科学我们将通过创新预测方法来实现这一目标 以及对预报的最佳沟通和可视化进行系统研究。合作 与美国疾病控制和预防中心在这项合作协议中，我们的目标是 扩展现有的方法，以纳入新的数据源和模型结构，从而 提高美国流感预测的准确性。 我们将审查和修订现有的FluSight预测指南、目标和准确性评估， 国家、地区和州一级。这将包括与利益攸关方进行访谈， 联邦、州和地方流行病学家跟踪预测和结果的使用情况， 目前预测的未满足需求。我们将开发和完善创建预测的方法 合奏，特别侧重于开发合奏加权计划，使用强大的，惩罚 方法来估计模型权重。我们将确定方法和数据来源， 开始和高峰周预测，高峰强度和短期预测的预测精度 国家、地区和州一级。我们的工作将集中在开发多尺度空间模型 利用来自公共和私人来源的州和邮政编码级别的流感感染数据。 我们将开发通信产品和方法来描述预测结果和不确定性， 联邦和州公共卫生官员和公众。我们将通过整合新的 可视化到我们现有的交互式数据可视化产品中，用于美国的流感预测 系统地研究终端用户对各种可视化和数据呈现的感知 布局最后，我们将开发和调整成功的季节性方法，数据源， 通信方法预测的时间，强度，和短期轨迹的一个 流感大流行。具体来说，我们将创建、测试和传播每周数据 来自美国报告的流感病例的最新来源的摘要和可视化 （包括来自我们的实时床旁数据源的数据），并验证我们的新空间模型 与模拟的大流行数据进行对比。