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.

项目摘要/摘要 该项目的核心目的是作为预测研究的强大，跨学科的中心 和科学。我们将通过开发预测方法的创新来实现这一目标 以及对预测的最佳沟通和可视化的系统研究。通过工作 随着美国疾病控制与预防中心的这一合作协议，我们的目标是 扩展现有的方法以合并新的数据源和模型结构，从而 提高美国的流感预测准确性。 我们将审查并修改现有的Flusight预测指导，目标和准确评估 国家，地区和州一级。这将包括与 联邦，州和地方流行病学家跟踪预测和结果的用途并确定 目前的预测未满足。我们将开发并完善方法来创建预测 合奏，特别着眼于使用强大的惩罚来制定合奏加权方案 估计模型权重的方法。我们将确定增加的方法和数据源 预测开始和高峰周的预测准确性预测，峰值强度和短期预测 国家，地区和州一级。我们在这里的工作将着重于开发多尺度空间模型 这种利用了来自公共和私人资源的流感感染的状态和邮政编码水平数据。 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以及公众。我们将通过合并新来实现这一目标 在美国的流感预测中，可视化我们现有的交互式数据可视化产品 并系统地研究各种可视化和数据表示的最终用户感知 布局。最后，我们将开发并适应成功的季节性方法，数据源和 沟通方法，以预测一个时间，强度和短期轨迹 新兴的流感大流行。具体而言，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 （包括来自我们实时保健数据源的数据），并验证我们的新空间模型 反对模拟的大流行数据。